Assignment_ML_L6.ipynb

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        "<a href=\"https://colab.research.google.com/github/chindimaga/SHALA2020/blob/master/Assignment_ML_L6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Assignment on Feature Engineering (L6)\n",
        "\n",
        "This assignment has been adapted from the course **Feature Engineering for Machine Learning in Python** On DataCamp. \n",
        "\n",
        "We will explore what feature engineering is and how to get started with applying it to real-world data. We will be working with a modified subset of the [Stackoverflow survey response data](https://insights.stackoverflow.com/survey/2018/#overview). This data set records the details, and preferences of thousands of users of the StackOverflow website. "
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Creating Features \n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xxrLsTxvi4Ef",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lw55w5YsB6jE"
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      "source": [
        "---\n",
        "### Inspect your data\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fCpuZS41h35f",
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      "source": [
        "# Load the data which has been stored as a CSV on the URL given below:\n",
        "so_survey_csv = 'https://assets.datacamp.com/production/repositories/3752/datasets/19699a2441073ad6459bf5e3e17690e2cae86cf1/Combined_DS_v10.csv'\n",
        "\n",
        "# Import so_survey_csv into so_survey_df\n",
        "so_survey_df = pd.read_csv(so_survey_csv)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yO7TR7EGBgDP"
      },
      "source": [
        "Instructions: For the `so_survey_df` DataFrame, \n",
        "* Print its shape and its first five rows. \n",
        "* Print the data type of each column.  "
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "6e82dcad-b566-4425-e9ed-499965c2d340"
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      "source": [
        "print(f\"The shape of so_survey_df is {so_survey_df.shape}\")\n",
        "so_survey_df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of so_survey_df is (999, 11)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SurveyDate</th>\n",
              "      <th>FormalEducation</th>\n",
              "      <th>ConvertedSalary</th>\n",
              "      <th>Hobby</th>\n",
              "      <th>Country</th>\n",
              "      <th>StackOverflowJobsRecommend</th>\n",
              "      <th>VersionControl</th>\n",
              "      <th>Age</th>\n",
              "      <th>Years Experience</th>\n",
              "      <th>Gender</th>\n",
              "      <th>RawSalary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2/28/18 20:20</td>\n",
              "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Git</td>\n",
              "      <td>21</td>\n",
              "      <td>13</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6/28/18 13:26</td>\n",
              "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
              "      <td>70841.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sweeden</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Git;Subversion</td>\n",
              "      <td>38</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>70,841.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6/6/18 3:37</td>\n",
              "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Sweeden</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Git</td>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5/9/18 1:06</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>21426.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sweeden</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zip file back-ups</td>\n",
              "      <td>46</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
              "      <td>21,426.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4/12/18 22:41</td>\n",
              "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
              "      <td>41671.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>UK</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Git</td>\n",
              "      <td>39</td>\n",
              "      <td>7</td>\n",
              "      <td>Male</td>\n",
              "      <td>£41,671.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      SurveyDate                                    FormalEducation  \\\n",
              "0  2/28/18 20:20           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
              "1  6/28/18 13:26           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
              "2    6/6/18 3:37           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
              "3    5/9/18 1:06  Some college/university study without earning ...   \n",
              "4  4/12/18 22:41           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
              "\n",
              "   ConvertedSalary Hobby       Country  StackOverflowJobsRecommend  \\\n",
              "0              NaN   Yes  South Africa                         NaN   \n",
              "1          70841.0   Yes       Sweeden                         7.0   \n",
              "2              NaN    No       Sweeden                         8.0   \n",
              "3          21426.0   Yes       Sweeden                         NaN   \n",
              "4          41671.0   Yes            UK                         8.0   \n",
              "\n",
              "      VersionControl  Age  Years Experience Gender   RawSalary  \n",
              "0                Git   21                13   Male         NaN  \n",
              "1     Git;Subversion   38                 9   Male   70,841.00  \n",
              "2                Git   45                11    NaN         NaN  \n",
              "3  Zip file back-ups   46                12   Male   21,426.00  \n",
              "4                Git   39                 7   Male  £41,671.00  "
            ]
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        "outputId": "2fb23fe9-9629-442f-80d7-2a36beb94fff"
      },
      "source": [
        "so_survey_df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SurveyDate                     object\n",
              "FormalEducation                object\n",
              "ConvertedSalary               float64\n",
              "Hobby                          object\n",
              "Country                        object\n",
              "StackOverflowJobsRecommend    float64\n",
              "VersionControl                 object\n",
              "Age                             int64\n",
              "Years Experience                int64\n",
              "Gender                         object\n",
              "RawSalary                      object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1MzXp3T8Cl8W"
      },
      "source": [
        "---\n",
        "### Selecting specific data types\n",
        "---\n",
        "Often a data set will contain columns with several different data types (like the one we are working with). The majority of machine learning models require us to have a consistent data type across features. Similarly, most feature engineering techniques are applicable to only one type of data at a time. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "glnEQlrVCobL"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Create a subset of `so_survey_df` consisting of only the numeric (int and float) columns and save it as `so_numeric_df`. \n",
        "* Print the column names contained in `so_numeric_df`. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOPjg0BBbw6n",
        "colab_type": "code",
        "colab": {},
        "outputId": "36cb1b4e-d206-4710-87ab-b22e000ce048"
      },
      "source": [
        "so_numeric_df = so_survey_df.select_dtypes(include=['int' ,'float'])\n",
        "print(\"Numeric (int and float) columns: \",so_numeric_df.columns.tolist())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Numeric (int and float) columns:  ['ConvertedSalary', 'StackOverflowJobsRecommend', 'Age', 'Years Experience']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O_4kpncQDXuj"
      },
      "source": [
        "---\n",
        "### One-hot encoding and dummy variables\n",
        "---\n",
        "To use categorical variables in a machine learning model, we first need to represent them in a quantitative way. The two most common approaches are to one-hot encode the variables using or to use dummy variables. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oRQM6bVaDhnT"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* One-hot encode the `Country` column of `so_survey_df` DataFrame, adding \"OH\" as a prefix for each column.\n",
        "* Create dummy variables for the `Country` column, adding \"DM\" as a prefix for each column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBT5Ix7qbw6x",
        "colab_type": "code",
        "colab": {},
        "outputId": "3dbe7a78-2d31-41ac-d67a-60eabf7de8c2"
      },
      "source": [
        "one_hot = pd.get_dummies(so_survey_df.Country , prefix='OH')\n",
        "print(one_hot.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['OH_France', 'OH_India', 'OH_Ireland', 'OH_Russia', 'OH_South Africa',\n",
            "       'OH_Spain', 'OH_Sweeden', 'OH_UK', 'OH_USA', 'OH_Ukraine'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOGMWAhYbw63",
        "colab_type": "code",
        "colab": {},
        "outputId": "5dfcda5b-cb39-4941-eaf8-a8e75b4494c4"
      },
      "source": [
        "#here france implies all zeros\n",
        "dummy = pd.get_dummies(so_survey_df.Country , drop_first=True, prefix='DM')\n",
        "print(dummy.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['DM_India', 'DM_Ireland', 'DM_Russia', 'DM_South Africa', 'DM_Spain',\n",
            "       'DM_Sweeden', 'DM_UK', 'DM_USA', 'DM_Ukraine'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UZ56-6j9oz4H"
      },
      "source": [
        "---\n",
        "### Dealing with uncommon categories\n",
        "---\n",
        "Some features can have many different categories but a very uneven distribution of their occurrences. Take for example Data Science's favorite languages to code in, some common choices are Python, R, and Julia, but there can be individuals with bespoke choices, like FORTRAN, C etc. In these cases, we may not want to create a feature for each value, but only the more common occurrences."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1z1TCKQ7EHcb"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Create a mask for values occurring less than 10 times in `country_counts`.\n",
        "* Print the first 5 rows of the mask.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0BgkHt2bw7A",
        "colab_type": "code",
        "colab": {},
        "outputId": "de8d3df2-e8d9-4761-ba52-598b7dd08340"
      },
      "source": [
        "countries = so_survey_df.Country\n",
        "country_counts = countries.value_counts()\n",
        "mask = countries.isin(country_counts[country_counts < 10].index)\n",
        "mask.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    False\n",
              "1    False\n",
              "2    False\n",
              "3    False\n",
              "4    False\n",
              "Name: Country, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6Zro-imOEiwt"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Label values occurring less than the `mask` cutoff as 'Other'.\n",
        "* Print the new category counts in `countries`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMPgeI9Rbw7G",
        "colab_type": "code",
        "colab": {},
        "outputId": "6a8da901-ae4d-4e61-96f3-21d01cd4e454"
      },
      "source": [
        "countries[mask]= 'Others'\n",
        "countries.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "South Africa    166\n",
              "USA             164\n",
              "Spain           134\n",
              "Sweeden         119\n",
              "France          115\n",
              "Russia           97\n",
              "UK               95\n",
              "India            95\n",
              "Others           14\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "w0j7IlzLpke5"
      },
      "source": [
        "---\n",
        "### Binarizing columns\n",
        "---\n",
        "While numeric values can often be used without any feature engineering, there will be cases when some form of manipulation can be useful. For example on some occasions, we might not care about the magnitude of a value but only care about its direction, or if it exists at all. In these situations, we will want to binarize a column. In the `so_survey_df` data, we have a large number of survey respondents that are working voluntarily (without pay). We will create a new column titled `Paid_Job` indicating whether each person is paid (their salary is greater than zero)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HdCPaTzFExzS"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Create a new column called `Paid_Job` filled with zeros.\n",
        "* Replace all the `Paid_Job` values with a 1 where the corresponding `ConvertedSalary` is greater than 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61RSAmJ5bw7O",
        "colab_type": "code",
        "colab": {},
        "outputId": "8dff2ead-2ff7-4572-b7ca-2726803f9e75"
      },
      "source": [
        "# paid_job = (so_survey_df.ConvertedSalary > 0).astype(int)\n",
        "so_survey_df['Paid_Job']=0\n",
        "so_survey_df.Paid_Job[so_survey_df['ConvertedSalary'] > 0]=1\n",
        "so_survey_df[['ConvertedSalary','Paid_Job']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConvertedSalary</th>\n",
              "      <th>Paid_Job</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>70841.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21426.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41671.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ConvertedSalary  Paid_Job\n",
              "0              NaN         0\n",
              "1          70841.0         1\n",
              "2              NaN         0\n",
              "3          21426.0         1\n",
              "4          41671.0         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7lJTmpvzq_NJ"
      },
      "source": [
        "---\n",
        "### Binning values\n",
        "---\n",
        "For many continuous values we will care less about the exact value of a numeric column, but instead care about the bucket it falls into. This can be useful when plotting values, or simplifying your machine learning models. It is mostly used on continuous variables where accuracy is not the biggest concern e.g. age, height, wages.\n",
        "\n",
        "Bins are created using `pd.cut(df['column_name'], bins)` where bins can be an integer specifying the number of evenly spaced bins, or a list of bin boundaries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GglGS3NkFtU3"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Bin the value of the `ConvertedSalary` column in `so_survey_df` into 5 equal bins, in a new column called `equal_binned`.\n",
        "* Print the first five rows of both columns: `ConvertedSalary` and `equal_binned`. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6HD9Uh2bw7V",
        "colab_type": "code",
        "colab": {},
        "outputId": "cc803db2-03e3-4fc2-ced3-f47a8e47f2f6"
      },
      "source": [
        "bins = 5\n",
        "so_survey_df['equal_binned'] = pd.cut(so_survey_df['ConvertedSalary'], bins)\n",
        "so_survey_df[['ConvertedSalary','equal_binned']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConvertedSalary</th>\n",
              "      <th>equal_binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>70841.0</td>\n",
              "      <td>(-2000.0, 400000.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21426.0</td>\n",
              "      <td>(-2000.0, 400000.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41671.0</td>\n",
              "      <td>(-2000.0, 400000.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ConvertedSalary         equal_binned\n",
              "0              NaN                  NaN\n",
              "1          70841.0  (-2000.0, 400000.0]\n",
              "2              NaN                  NaN\n",
              "3          21426.0  (-2000.0, 400000.0]\n",
              "4          41671.0  (-2000.0, 400000.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-o_1_UiiF1dc"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Bin the `ConvertedSalary` column using the boundaries in the list bins and label the bins using `labels` in a new column called `boundary_binned`. \n",
        "* Print the first 5 rows of the `boundary_binned` column. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UZyNvV6krVLJ",
        "colab": {},
        "outputId": "e5889b0d-6d48-4341-9ca2-75c4beb3cd19"
      },
      "source": [
        "# Specify the boundaries of the bins\n",
        "bins = [-np.inf, 10000, 50000, 100000, 150000, np.inf]\n",
        "\n",
        "# Bin labels\n",
        "labels = ['Very low', 'Low', 'Medium', 'High', 'Very high']\n",
        "\n",
        "so_survey_df['boundary_binned'] = pd.cut(so_survey_df['ConvertedSalary'], bins=bins, labels=labels)\n",
        "so_survey_df[['ConvertedSalary','boundary_binned']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConvertedSalary</th>\n",
              "      <th>boundary_binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>70841.0</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21426.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41671.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ConvertedSalary boundary_binned\n",
              "0              NaN             NaN\n",
              "1          70841.0          Medium\n",
              "2              NaN             NaN\n",
              "3          21426.0             Low\n",
              "4          41671.0             Low"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cIkFDAVUGhcQ"
      },
      "source": [
        "## Dealing with Messy Data\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "m8o-Z4YEsYF9"
      },
      "source": [
        "---\n",
        "### How sparse is my data?\n",
        "---\n",
        "Most data sets contain missing values, often represented as NaN (Not a Number). If we are working with Pandas, we can easily check how many missing values exist in each column.\n",
        "\n",
        "Let's find out how many of the developers taking the survey chose to enter their age (found in the `Age` column of `so_survey_df`) and their gender (`Gender` column of `so_survey_df`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1n0JDxzMHMwD"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Subset the `so_survey_df` DataFrame to only include the `Age` and `Gender` columns.\n",
        "* Print the number of non-missing values in both columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrvpA2G_bw7m",
        "colab_type": "code",
        "colab": {},
        "outputId": "2ac7c3cd-6e8d-4b8b-8cec-6420fe657869"
      },
      "source": [
        "sub_df = so_survey_df[['Age' , 'Gender']]\n",
        "print(sub_df.notnull().sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Age       999\n",
            "Gender    693\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "STDb03R3swA4"
      },
      "source": [
        "---\n",
        "### Finding the missing values\n",
        "---\n",
        "While having a summary of how much of your data is missing can be useful, often we will need to find the exact locations of these missing values. Using the same subset of the StackOverflow data from the last exercise (`sub_df`), we will show how a value can be flagged as missing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E5XbMPSrHhY4"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Print the first 10 entries of the `sub_df` DataFrame.\n",
        "* Print the locations of the missing values in the first 10 rows of this DataFrame.\n",
        "* Print the locations of the non-missing values in the first 10 rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRTq_Cinbw7v",
        "colab_type": "code",
        "colab": {},
        "outputId": "b9b93c02-ce23-438d-d9d6-6d2c483e7524"
      },
      "source": [
        "sub_df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>39</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>34</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>24</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>23</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Gender\n",
              "0   21    Male\n",
              "1   38    Male\n",
              "2   45     NaN\n",
              "3   46    Male\n",
              "4   39    Male\n",
              "5   39    Male\n",
              "6   34    Male\n",
              "7   24  Female\n",
              "8   23    Male\n",
              "9   36     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OxCzUtmbw70",
        "colab_type": "code",
        "colab": {},
        "outputId": "c62f392b-bf68-465d-dbe3-7aa640eec5b3"
      },
      "source": [
        "sub_df.head(10).isnull()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age  Gender\n",
              "0  False   False\n",
              "1  False   False\n",
              "2  False    True\n",
              "3  False   False\n",
              "4  False   False\n",
              "5  False   False\n",
              "6  False   False\n",
              "7  False   False\n",
              "8  False   False\n",
              "9  False    True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNvCLiT8bw77",
        "colab_type": "code",
        "colab": {},
        "outputId": "2e01c2fe-9058-43f6-b988-3229ba6cec29"
      },
      "source": [
        "sub_df.head(10).notnull()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Gender\n",
              "0  True    True\n",
              "1  True    True\n",
              "2  True   False\n",
              "3  True    True\n",
              "4  True    True\n",
              "5  True    True\n",
              "6  True    True\n",
              "7  True    True\n",
              "8  True    True\n",
              "9  True   False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CgR_uodEtS2o"
      },
      "source": [
        "---\n",
        "### Listwise deletion\n",
        "---\n",
        "The simplest way to deal with missing values in our dataset when they are occurring entirely at random is to remove those rows, also called 'listwise deletion'.\n",
        "\n",
        "Depending on the use case, we will sometimes want to remove all missing values in our data while other times we may want to only remove a particular column if too many values are missing in that column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kIioVg70H8fY"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Print the number of rows and columns in `so_survey_df`.\n",
        "* Drop all rows with missing values in `so_survey_df`.\n",
        "* Drop all columns with missing values in `so_survey_df`.\n",
        "* Drop all rows in `so_survey_df` where `Gender` is missing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoC6bxQ3bw8C",
        "colab_type": "code",
        "colab": {},
        "outputId": "ce3eace6-87b7-4133-9101-254ef8ea89f2"
      },
      "source": [
        "print(f\"No of: \\n rows:{so_survey_df.shape[0]} \\n columns:{so_survey_df.shape[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of: \n",
            " rows:999 \n",
            " columns:14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0ZDLJ-5bw8H",
        "colab_type": "code",
        "colab": {},
        "outputId": "8f91dca3-f3d2-47ea-f959-742931e01887"
      },
      "source": [
        "no_missing = so_survey_df.dropna()\n",
        "print(f\"No of: \\n rows:{no_missing.shape[0]} \\n columns:{no_missing.shape[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of: \n",
            " rows:264 \n",
            " columns:14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HaAvoC5bw8L",
        "colab_type": "code",
        "colab": {},
        "outputId": "93a8880f-ff4a-4322-aab1-6932ae497d42"
      },
      "source": [
        "no_missing_cols = so_survey_df.dropna(how='any', axis=1)\n",
        "print(f\"No of: \\n rows:{no_missing_cols.shape[0]} \\n columns:{no_missing_cols.shape[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of: \n",
            " rows:999 \n",
            " columns:8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FCUGQxRbw8P",
        "colab_type": "code",
        "colab": {},
        "outputId": "0660efed-5631-4d5b-cadd-9f1c99411321"
      },
      "source": [
        "no_gender = so_survey_df.dropna(subset=['Gender'])\n",
        "print(f\"No of: \\n rows:{no_gender.shape[0]} \\n columns:{no_gender.shape[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of: \n",
            " rows:693 \n",
            " columns:14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9C1H8JwhtxHU"
      },
      "source": [
        "---\n",
        "### Replacing missing values with constants\n",
        "---\n",
        "While removing missing data entirely maybe a correct approach in many situations, this may result in a lot of information being omitted from your models.\n",
        "\n",
        "We may find categorical columns where the missing value is a valid piece of information in itself, such as someone refusing to answer a question in a survey. In these cases, we can fill all missing values with a new category entirely, for example 'No response given'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cPq_Ae40IUnY"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Print the count of occurrences of each category in `so_survey_df`'s `Gender` column.\n",
        "* Replace all missing values in the `Gender` column with the string 'Not Given'. Make changes to the original DataFrame.\n",
        "* Print the count of occurrences of updated category in `so_survey_df`'s `Gender` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSZEzi1Pbw8Y",
        "colab_type": "code",
        "colab": {},
        "outputId": "ae90455b-a650-4ad6-959d-536699637067"
      },
      "source": [
        "so_survey_df.Gender.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male                                                                         632\n",
              "Female                                                                        53\n",
              "Transgender                                                                    2\n",
              "Female;Male                                                                    2\n",
              "Female;Male;Transgender;Non-binary. genderqueer. or gender non-conforming      1\n",
              "Male;Non-binary. genderqueer. or gender non-conforming                         1\n",
              "Non-binary. genderqueer. or gender non-conforming                              1\n",
              "Female;Transgender                                                             1\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqZZDHeUbw8d",
        "colab_type": "code",
        "colab": {},
        "outputId": "5a8f10a0-dd5a-4dfd-9d48-770449d50608"
      },
      "source": [
        "so_survey_df.Gender.fillna('Not given', inplace=True)\n",
        "so_survey_df.Gender.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male                                                                         632\n",
              "Not given                                                                    306\n",
              "Female                                                                        53\n",
              "Transgender                                                                    2\n",
              "Female;Male                                                                    2\n",
              "Female;Male;Transgender;Non-binary. genderqueer. or gender non-conforming      1\n",
              "Male;Non-binary. genderqueer. or gender non-conforming                         1\n",
              "Non-binary. genderqueer. or gender non-conforming                              1\n",
              "Female;Transgender                                                             1\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Rn2h-F8IuI0C"
      },
      "source": [
        "---\n",
        "### Filling continuous missing values\n",
        "---\n",
        "Earlier, we dealt with different methods of removing data missing values and filling in missing values with a fixed string. These approaches are valid in many cases, particularly when dealing with categorical columns but have limited use when working with continuous values. In these cases, it may be most valid to fill the missing values in the column with a value calculated from the entries present in the column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_S9YZ2GsIyLx"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Print the first five rows of the `StackOverflowJobsRecommend` column of `so_survey_df`.\n",
        "* Replace the missing values in the `StackOverflowJobsRecommend` column with its mean. Make changes directly to the original DataFrame.\n",
        "* Round the decimal values that we introduced in the `StackOverflowJobsRecommend` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1htNenaibw8m",
        "colab_type": "code",
        "colab": {},
        "outputId": "560724ff-62af-4dc9-b6e8-8f42ddd3066b"
      },
      "source": [
        "so_survey_df[\"StackOverflowJobsRecommend\"].head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    NaN\n",
              "1    7.0\n",
              "2    8.0\n",
              "3    NaN\n",
              "4    8.0\n",
              "Name: StackOverflowJobsRecommend, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYWHai6dbw8r",
        "colab_type": "code",
        "colab": {},
        "outputId": "5903da08-4f16-4391-d3c9-2c6d3b14890e"
      },
      "source": [
        "so_survey_df[\"StackOverflowJobsRecommend\"].fillna(so_survey_df[\"StackOverflowJobsRecommend\"].mean(),inplace=True)\n",
        "so_survey_df[\"StackOverflowJobsRecommend\"].head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7.061602\n",
              "1    7.000000\n",
              "2    8.000000\n",
              "3    7.061602\n",
              "4    8.000000\n",
              "Name: StackOverflowJobsRecommend, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbtqmpT7bw8w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so_survey_df[\"StackOverflowJobsRecommend\"]=round(so_survey_df[\"StackOverflowJobsRecommend\"])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SKcC12f0vCuh"
      },
      "source": [
        "---\n",
        "### Dealing with stray characters (I)\n",
        "---\n",
        "In this exercise, we will work with the `RawSalary` column of so_survey_df which contains the wages of the respondents along with the currency symbols and commas, such as $42,000. When importing data from Microsoft Excel, more often that not we will come across data in this form."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tz0CGSQkJI03"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Remove the commas (,) from the `RawSalary` column.\n",
        "* Remove the dollar ($) signs from the `RawSalary` column.\n",
        "* Print the first five rows of updated `RawSalary` column. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqj0xf_9bw85",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so_survey_df[\"RawSalary\"] = so_survey_df[\"RawSalary\"].str.replace(\",\",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jgtiSPsbw8-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so_survey_df[\"RawSalary\"] = so_survey_df[\"RawSalary\"].str.replace(\"$\",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5QjYp-Pbw9D",
        "colab_type": "code",
        "colab": {},
        "outputId": "71a0d78b-5d3f-40ef-8f18-a19028b69409"
      },
      "source": [
        "so_survey_df[\"RawSalary\"].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          NaN\n",
              "1     70841.00\n",
              "2          NaN\n",
              "3     21426.00\n",
              "4    £41671.00\n",
              "Name: RawSalary, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UpayqY5IwMBl"
      },
      "source": [
        "---\n",
        "### Dealing with stray characters (II)\n",
        "---\n",
        "In the last exercise, we could tell quickly based off of the `df.head()` call which characters were causing an issue. In many cases this will not be so apparent. There will often be values deep within a column that are preventing us from casting a column as a numeric type so that it can be used in a model or further feature engineering.\n",
        "\n",
        "One approach to finding these values is to force the column to the data type desired using `pd.to_numeric()`, coercing any values causing issues to `NaN`, Then filtering the DataFrame by just the rows containing the `NaN` values.\n",
        "\n",
        "Try to cast the `RawSalary` column as a float and it will fail as an additional character can now be found in it. Find the character and remove it so the column can be cast as a float."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z7Duf4v7JpPv"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Attempt to convert the `RawSalary` column of `so_survey_df` to numeric values coercing all failures into null values.\n",
        "* Find the indexes of the rows containing `NaN`s.\n",
        "* Print the rows in `RawSalary` based on these indexes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AYuh53vbv5_d",
        "outputId": "7f02eefc-b7da-485b-c391-dcab68a352c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# Attempt to convert the column to numeric values\n",
        "numeric_vals = pd.to_numeric(so_survey_df['RawSalary'], errors='coerce')\n",
        "idx = numeric_vals.isna()\n",
        "print(so_survey_df.RawSalary[idx])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0            NaN\n",
            "2            NaN\n",
            "4      £41671.00\n",
            "6            NaN\n",
            "8            NaN\n",
            "         ...    \n",
            "989          NaN\n",
            "990          NaN\n",
            "992          NaN\n",
            "994          NaN\n",
            "997          NaN\n",
            "Name: RawSalary, Length: 401, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vyllnJ5NKEwy"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Did you notice the pound (£) signs in the `RawSalary` column? Remove these signs like we did in the previous exercise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOr72dbLbw9Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so_survey_df[\"RawSalary\"] = so_survey_df[\"RawSalary\"].str.replace(\"£\",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "feXhsa94wtBz"
      },
      "source": [
        "---\n",
        "### Method chaining\n",
        "---\n",
        "When applying multiple operations on the same column (like in the previous exercises), you made the changes in several steps, assigning the results back in each step. However, when applying multiple successive operations on the same column, you can \"chain\" these operations together for clarity and ease of management. This can be achieved by calling multiple methods sequentially:\n",
        "```\n",
        "# Method chaining\n",
        "df['column'] = df['column'].method1().method2().method3()\n",
        "\n",
        "# Same as \n",
        "df['column'] = df['column'].method1()\n",
        "df['column'] = df['column'].method2()\n",
        "df['column'] = df['column'].method3()\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yo-JUG77KTRi"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Remove the commas (`,`) from the `RawSalary` column of `so_survey_df`.\n",
        "* Remove the dollar (`$`) signs from the `RawSalary` column.\n",
        "* Remove the pound (`£`) signs from the `RawSalary` column.\n",
        "* Convert the `RawSalary` column to float."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvD7Es5ybw9Y",
        "colab_type": "code",
        "colab": {},
        "outputId": "edbf2017-92c3-47ee-d06d-493b774a2d72"
      },
      "source": [
        "so_survey_df[\"RawSalary\"] = so_survey_df[\"RawSalary\"].str.replace(\",\",\"\")\\\n",
        "                                                      .str.replace(\"£\",\"\")\\\n",
        "                                                        .str.replace(\"$\",\"\")\\\n",
        "                                                        .astype(float)\n",
        "so_survey_df.RawSalary.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        NaN\n",
              "1    70841.0\n",
              "2        NaN\n",
              "3    21426.0\n",
              "4    41671.0\n",
              "Name: RawSalary, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CqpCNv72Kl_1"
      },
      "source": [
        "## Conforming to Statistical Assumptions\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hYwlN2FyyBml"
      },
      "source": [
        "---\n",
        "### What does your data look like?\n",
        "---\n",
        "Up until now we have focused on creating new features and dealing with issues in our data. Feature engineering can also be used to make the most out of the data that we already have and use it more effectively when creating machine learning models.\n",
        "Many algorithms may assume that our data is normally distributed, or at least that all our columns are on the same scale. This will often not be the case, e.g. one feature may be measured in thousands of dollars while another would be number of years. In this exercise, we will create plots to examine the distributions of some numeric columns in the `so_survey_df` DataFrame. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LHMvrvwxyTQu",
        "colab": {}
      },
      "source": [
        "so_numeric_df = so_survey_df[['ConvertedSalary', 'Age', 'Years Experience']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i8Ch5slQy6B-"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Generate a histogram of all columns in the `so_numeric_df` DataFrame.\n",
        "* Generate box plots of the `Age` and `Years Experience` columns in the `so_numeric_df` DataFrame.\n",
        "* Generate a box plot of the `ConvertedSalary` column in the `so_numeric_df`.\n",
        "* Plot pairwise relationships (using `sns.pairplot`) in the `so_numeric_df`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JF37ix1bw9n",
        "colab_type": "code",
        "colab": {},
        "outputId": "a115ad1c-d545-494e-cb5a-2aabf1df896d"
      },
      "source": [
        "so_numeric_df.hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGRxd0A5bw9u",
        "colab_type": "code",
        "colab": {},
        "outputId": "1e946bb1-9b30-4c95-9543-1727478df45c"
      },
      "source": [
        "so_numeric_df[['Age' , 'Years Experience']].boxplot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpUFLzmfbw90",
        "colab_type": "code",
        "colab": {},
        "outputId": "0171716c-fc6e-4cd0-9089-a18c0b47512e"
      },
      "source": [
        "so_numeric_df[['ConvertedSalary']].boxplot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dr9Vb2pibw97",
        "colab_type": "code",
        "colab": {},
        "outputId": "0c5db96b-03f8-43ac-e8c2-849fddb60b7a"
      },
      "source": [
        "sns.pairplot(so_numeric_df);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "op7uFM-70CW5"
      },
      "source": [
        "---\n",
        "### Normalization\n",
        "---\n",
        "In normalization we linearly scale the entire column between 0 and 1, with 0 corresponding with the lowest value in the column, and 1 with the largest. When using scikit-learn (the most commonly used machine learning library in Python) we can use a `MinMaxScaler` to apply normalization. (It is called this as it scales our values between a minimum and maximum value.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ME9djjbu0K83"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Fit the `MinMaxScaler` on the `Age` column of `so_numeric_df`.\n",
        "* Transform the same column with the scaler you just fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jNgwBJRp0Zgl",
        "colab": {}
      },
      "source": [
        "# Import MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iYeEJLVQ0HCU",
        "colab": {},
        "outputId": "3dca73d3-2f6b-4a78-f3b2-20483787a647"
      },
      "source": [
        "# Instantiate MinMaxScaler\n",
        "MM_scaler = MinMaxScaler()\n",
        "\n",
        "MM_scaler.fit(so_numeric_df[['Age']])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler(copy=True, feature_range=(0, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h95xPBQDbw-M",
        "colab_type": "code",
        "colab": {},
        "outputId": "c509112a-8db2-4b15-ff8b-13155f41ff28"
      },
      "source": [
        "so_numeric_df['Age_MM'] = MM_scaler.transform(so_numeric_df[['Age']])\n",
        "so_numeric_df[['Age','Age_MM']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Age_MM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>0.046154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>0.307692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>0.415385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46</td>\n",
              "      <td>0.430769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>39</td>\n",
              "      <td>0.323077</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age    Age_MM\n",
              "0   21  0.046154\n",
              "1   38  0.307692\n",
              "2   45  0.415385\n",
              "3   46  0.430769\n",
              "4   39  0.323077"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lBKJrNv60m2p"
      },
      "source": [
        "---\n",
        "### Standardization\n",
        "---\n",
        "While normalization can be useful for scaling a column between two data points, it is hard to compare two scaled columns if even one of them is overly affected by outliers. One commonly used solution to this is called standardization, where instead of having a strict upper and lower bound, you center the data around its mean, and calculate the number of standard deviations away from mean each data point is."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gFyXLW3v0rU9"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Fit the `StandardScaler` on the `Age` column of `so_numeric_df`.\n",
        "* Transform the same column with the scaler we just fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TL8N_V9n03jy",
        "colab": {}
      },
      "source": [
        "# Import StandardScaler\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lOoRs_vR0qmJ",
        "colab": {},
        "outputId": "95e0a070-929d-4d1b-afb4-421171180bf1"
      },
      "source": [
        "# Instantiate StandardScaler\n",
        "SS_scaler = StandardScaler()\n",
        "SS_scaler.fit(so_numeric_df[['Age']])\n",
        "so_numeric_df['Age_SS'] = SS_scaler.transform(so_numeric_df[['Age']])\n",
        "so_numeric_df[['Age','Age_SS']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Age_SS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>-1.132431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>0.150734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>0.679096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46</td>\n",
              "      <td>0.754576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>39</td>\n",
              "      <td>0.226214</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age    Age_SS\n",
              "0   21 -1.132431\n",
              "1   38  0.150734\n",
              "2   45  0.679096\n",
              "3   46  0.754576\n",
              "4   39  0.226214"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QLntVWcp1aZ1"
      },
      "source": [
        "---\n",
        "### Log transformation\n",
        "---\n",
        "In the previous exercises we scaled the data linearly, which will not affect the data's shape. This works great if our data is normally distributed (or closely normally distributed), an assumption that a lot of machine learning models make. Sometimes we will work with data that closely conforms to normality, e.g the height or weight of a population. On the other hand, many variables in the real world do not follow this pattern e.g, wages or age of a population. \n",
        "\n",
        "Now, we will use a log transform on the `ConvertedSalary` column in the `so_numeric_df` DataFrame as it has a large amount of its data centered around the lower values, but contains very high values also. These distributions are said to have a long right tail."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NMQB-F4e1lMK"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Fit the `PowerTransformer` on the `ConvertedSalary` column of `so_numeric_df`.\n",
        "* Transform the same column with the scaler we just fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "N95T8tjM1h5A",
        "colab": {}
      },
      "source": [
        "# Import PowerTransformer\n",
        "from sklearn.preprocessing import PowerTransformer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9CNzVVOg1ueY",
        "colab": {},
        "outputId": "5fea7cb2-a4ad-4742-d237-6b42bde88be8"
      },
      "source": [
        "# Instantiate PowerTransformer\n",
        "pow_trans = PowerTransformer()\n",
        "pow_trans.fit(so_numeric_df[['ConvertedSalary']])\n",
        "so_numeric_df['ConvertedSalary_LS'] = pow_trans.transform(so_numeric_df[['ConvertedSalary']])\n",
        "so_numeric_df[['ConvertedSalary','ConvertedSalary_LS']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConvertedSalary</th>\n",
              "      <th>ConvertedSalary_LS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>70841.0</td>\n",
              "      <td>0.312939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21426.0</td>\n",
              "      <td>-0.652182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41671.0</td>\n",
              "      <td>-0.135589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ConvertedSalary  ConvertedSalary_LS\n",
              "0              NaN                 NaN\n",
              "1          70841.0            0.312939\n",
              "2              NaN                 NaN\n",
              "3          21426.0           -0.652182\n",
              "4          41671.0           -0.135589"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PChRaqgmbw-l",
        "colab_type": "code",
        "colab": {},
        "outputId": "fa5f5c9b-70e2-4cc7-f3d9-32a557387ffe"
      },
      "source": [
        "so_numeric_df[['ConvertedSalary','ConvertedSalary_LS']].hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VhlDOrtb18uG"
      },
      "source": [
        "---\n",
        "### Percentage based outlier removal\n",
        "---\n",
        "One way to ensure a small portion of data is not having an overly adverse effect is by removing a certain percentage of the largest and/or smallest values in the column. This can be achieved by finding the relevant quantile and trimming the data using it with a mask. This approach is particularly useful if we are concerned that the highest values in our dataset should be avoided. When using this approach, we must remember that even if there are no outliers, this will still remove the same top N percentage from the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AhiOqx-v2CFB"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Find the 95th quantile of the `ConvertedSalary` column in `so_numeric_df` DataFrame.\n",
        "* Trim the `so_numeric_df` DataFrame to retain all rows where `ConvertedSalary` is less than it's 95th quantile and store this as `trimmed_df`.\n",
        "* Plot the histogram of `so_numeric_df[['ConvertedSalary']]`.\n",
        "* Plot the histogram of `trimmed_df[['ConvertedSalary']]`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G1_-eGVu1_JA",
        "colab": {},
        "outputId": "ac2ce0e2-4b90-45cb-be82-c59362768d75"
      },
      "source": [
        "# Find the 95th quantile\n",
        "quantile = so_numeric_df['ConvertedSalary'].quantile(0.95)\n",
        "\n",
        "trimmed_df = so_numeric_df[so_numeric_df['ConvertedSalary']<quantile]\n",
        "\n",
        "so_numeric_df['ConvertedSalary'].hist()\n",
        "plt.show()\n",
        "plt.clf()\n",
        "trimmed_df['ConvertedSalary'].hist()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lgva617FNqZy"
      },
      "source": [
        "## Dealing with Text Data\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IuHUqVR03PJV"
      },
      "source": [
        "---\n",
        "### Cleaning up your text\n",
        "---\n",
        "Unstructured text data cannot be directly used in most analyses. Multiple steps need to be taken to go from a long free form string to a set of numeric columns in the right format that can be ingested by a machine learning model. The first step of this process is to standardize the data and eliminate any characters that could cause problems later on in your analytic pipeline.\n",
        "\n",
        "Here, we will be working with a new dataset containing the inaugural speeches of the presidents of the United States loaded as `speech_df`, with the speeches stored in the `text` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dFmYNs7W3hYj",
        "outputId": "e5915ecf-c1d6-4d24-8953-35d4afc04e8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "speech_df = pd.read_csv('https://raw.githubusercontent.com/shala2020/shala2020.github.io/master/Lecture_Materials/Assignments/MachineLearning/L6/inaugural_speeches.csv')\n",
        "speech_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Inaugural Address</th>\n",
              "      <th>Date</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>George Washington</td>\n",
              "      <td>First Inaugural Address</td>\n",
              "      <td>Thursday, April 30, 1789</td>\n",
              "      <td>Fellow-Citizens of the Senate and of the House...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>George Washington</td>\n",
              "      <td>Second Inaugural Address</td>\n",
              "      <td>Monday, March 4, 1793</td>\n",
              "      <td>Fellow Citizens:  I AM again called upon by th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>John Adams</td>\n",
              "      <td>Inaugural Address</td>\n",
              "      <td>Saturday, March 4, 1797</td>\n",
              "      <td>WHEN it was first perceived, in early times, t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Thomas Jefferson</td>\n",
              "      <td>First Inaugural Address</td>\n",
              "      <td>Wednesday, March 4, 1801</td>\n",
              "      <td>Friends and Fellow-Citizens:  CALLED upon to u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thomas Jefferson</td>\n",
              "      <td>Second Inaugural Address</td>\n",
              "      <td>Monday, March 4, 1805</td>\n",
              "      <td>PROCEEDING, fellow-citizens, to that qualifica...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Name         Inaugural Address                      Date  \\\n",
              "0  George Washington   First Inaugural Address  Thursday, April 30, 1789   \n",
              "1  George Washington  Second Inaugural Address     Monday, March 4, 1793   \n",
              "2         John Adams         Inaugural Address   Saturday, March 4, 1797   \n",
              "3   Thomas Jefferson   First Inaugural Address  Wednesday, March 4, 1801   \n",
              "4   Thomas Jefferson  Second Inaugural Address     Monday, March 4, 1805   \n",
              "\n",
              "                                                text  \n",
              "0  Fellow-Citizens of the Senate and of the House...  \n",
              "1  Fellow Citizens:  I AM again called upon by th...  \n",
              "2  WHEN it was first perceived, in early times, t...  \n",
              "3  Friends and Fellow-Citizens:  CALLED upon to u...  \n",
              "4  PROCEEDING, fellow-citizens, to that qualifica...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qvbCog623SDx"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Print the first 5 rows of the `text` column in `speech_df` DataFrame to see the free text fields.\n",
        "* Replace all non letter characters in the `text` column with a whitespace and add it as a new column `text_clean` in the `speech_df` DataFrame. \n",
        "* Make all characters in the newly created `text_clean` column lower case.\n",
        "* Print the first 5 rows of the `text_clean` column. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FoY8YXzXbw_E",
        "colab_type": "code",
        "colab": {},
        "outputId": "5f3e967a-2cda-4c27-f5aa-a16e796dfae5"
      },
      "source": [
        "print(speech_df.text.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    Fellow-Citizens of the Senate and of the House...\n",
            "1    Fellow Citizens:  I AM again called upon by th...\n",
            "2    WHEN it was first perceived, in early times, t...\n",
            "3    Friends and Fellow-Citizens:  CALLED upon to u...\n",
            "4    PROCEEDING, fellow-citizens, to that qualifica...\n",
            "Name: text, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdjqiRSabw_I",
        "colab_type": "code",
        "colab": {},
        "outputId": "b77ac617-550b-4ff1-845c-907353f32b49"
      },
      "source": [
        "speech_df['text_clean'] = speech_df['text'].str.replace('[^a-zA-Z]',' ').str.lower()\n",
        "print(speech_df.text_clean.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    fellow citizens of the senate and of the house...\n",
            "1    fellow citizens   i am again called upon by th...\n",
            "2    when it was first perceived  in early times  t...\n",
            "3    friends and fellow citizens   called upon to u...\n",
            "4    proceeding  fellow citizens  to that qualifica...\n",
            "Name: text_clean, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ag6i4ijT4tcM"
      },
      "source": [
        "---\n",
        "### High level text features\n",
        "---\n",
        "Once the text has been cleaned and standardized we can begin creating features from the data. The most fundamental information we can calculate about free form text is its size, such as its length and number of words. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "q4jPqRmA4zRA"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Record the character length of each speech (`speech_df['text_clean']`) and store it in a new `char_count` column.\n",
        "* Record the word count of each speech in the `word_count` column.\n",
        "* Record the average word length of each speech in the `avg_word_length` column.\n",
        "* Print the first 5 rows of the columns: `text_clean`, `char_cnt`, `word_cnt`, `avg_word_length`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8e2fLQRfbw_N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "speech_df['char_cnt'] = speech_df['text_clean'].str.len()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUK_eEAzbw_T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "speech_df['word_cnt'] = speech_df['text_clean'].str.split().str.len()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9coVSnWbbw_Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "speech_df['avg_word_length'] = speech_df['char_cnt']/speech_df['word_cnt']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGRagTsobw_c",
        "colab_type": "code",
        "colab": {},
        "outputId": "b3377836-9169-4d2e-d563-7647533a23c7"
      },
      "source": [
        "speech_df[['text_clean', 'char_cnt', 'word_cnt', 'avg_word_length']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text_clean</th>\n",
              "      <th>char_cnt</th>\n",
              "      <th>word_cnt</th>\n",
              "      <th>avg_word_length</th>\n",
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            "text/plain": [
              "                                          text_clean  char_cnt  word_cnt  \\\n",
              "0  fellow citizens of the senate and of the house...      8616      1432   \n",
              "1  fellow citizens   i am again called upon by th...       787       135   \n",
              "2  when it was first perceived  in early times  t...     13871      2323   \n",
              "3  friends and fellow citizens   called upon to u...     10144      1736   \n",
              "4  proceeding  fellow citizens  to that qualifica...     12902      2169   \n",
              "\n",
              "   avg_word_length  \n",
              "0         6.016760  \n",
              "1         5.829630  \n",
              "2         5.971158  \n",
              "3         5.843318  \n",
              "4         5.948363  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Wi2hOh045bsm"
      },
      "source": [
        "---\n",
        "### Counting words (I)\n",
        "---\n",
        "Once high level information has been recorded we can begin creating features based on the actual content of each text, as given below: \n",
        "\n",
        "* For each unique word in the dataset a column is created.\n",
        "* For each entry, the number of times this word occurs is counted and the count value is entered into the respective column.\n",
        "\n",
        "These \"count\" columns can then be used to train machine learning models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b6sMPuHi5nB7"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Import `CountVectorizer` from `sklearn.feature_extraction.text`.\n",
        "* Instantiate `CountVectorizer` and assign it to 'cv'.\n",
        "* Fit the vectorizer to the `text_clean` column.\n",
        "* Print the feature names generated by the vectorizer and find the number of features.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nomeBUIc5lA2",
        "colab": {}
      },
      "source": [
        "# Import CountVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Instantiate CountVectorizer\n",
        "cv = CountVectorizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-t9WFrKbw_m",
        "colab_type": "code",
        "colab": {},
        "outputId": "bd510837-9dab-492f-b726-f2bf72d068d9"
      },
      "source": [
        "cv.fit(speech_df['text_clean'])\n",
        "print(len(cv.get_feature_names()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bAm6_JHr6Cto"
      },
      "source": [
        "---\n",
        "### Counting words (II)\n",
        "---\n",
        "Once the vectorizer has been fit to the data, it can be used to transform the text to an array representing the word counts. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JcFMh9k_6Hu4"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Apply the vectorizer ('cv' in the previous exercise) to the `text_clean` column.\n",
        "* Convert this transformed (sparse) array into a `numpy` array with counts and print it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faxrYcyQbw_s",
        "colab_type": "code",
        "colab": {},
        "outputId": "818a93d1-3017-4cf8-f27e-d2dc61319bc8"
      },
      "source": [
        "cv_transformed = cv.transform(speech_df['text_clean'])\n",
        "cv_array = cv_transformed.toarray()\n",
        "print(cv_array)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 1 0 ... 0 0 0]\n",
            " ...\n",
            " [0 1 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vUQAwgOV73BB"
      },
      "source": [
        "---\n",
        "### Limiting your features\n",
        "---\n",
        "As we have seen, using the `CountVectorizer` with its default settings creates a feature for every single word in our corpus. This can create far too many features, often including ones that will provide very little analytical value.\n",
        "\n",
        "For this purpose `CountVectorizer` has parameters that you can set to reduce the number of features:\n",
        "\n",
        "* `min_df` : Use only words that occur in more than this percentage of documents. This can be used to remove outlier words that will not generalize across texts.\n",
        "* `max_df` : Use only words that occur in less than this percentage of documents. This is useful to eliminate very common words that occur in every corpus without adding value such as \"and\" or \"the\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "I9X2x50g8Dfd"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Limit the number of features in the `CountVectorizer` by setting the minimum number of documents a word can appear to 20% and the maximum to 80%.\n",
        "* Fit and apply the vectorizer on `text_clean` column in one step.\n",
        "* Convert this transformed (sparse) array into a `numpy` array with counts and print the dimensions of the new reduced array.\n",
        "* Did you notice that the number of features (unique words) greatly reduced from 9043 to 818?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RqNCtZu-8NbI",
        "colab": {},
        "outputId": "69f8c010-b52b-4781-b94f-9f980cccf2fa"
      },
      "source": [
        "# Import CountVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Specify arguements to limit the number of features generated\n",
        "cv = CountVectorizer(min_df=0.2, max_df=0.8)\n",
        "cv.fit(speech_df['text_clean'])\n",
        "print(len(cv.get_feature_names()))\n",
        "cv_transformed = cv.transform(speech_df['text_clean'])\n",
        "cv_array = cv_transformed.toarray()\n",
        "# print(cv_array)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KKZ4xX3L9IxL"
      },
      "source": [
        "---\n",
        "### Text to DataFrame\n",
        "---\n",
        "Now that we have generated these count based features in an array we will need to reformat them so that they can be combined with the rest of the dataset. This can be achieved by converting the array into a pandas DataFrame, with the feature names you found earlier as the column names, and then concatenate it with the original DataFrame.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rvioGe8x9Olj"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Create a DataFrame `cv_df` containing the `cv_array` as the values and the feature names as the column names.\n",
        "* Add the prefix `Counts_` to the column names for ease of identification.\n",
        "* Concatenate this DataFrame (`cv_df`) to the original DataFrame (`speech_df`) column wise.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW9PzHRxbw_3",
        "colab_type": "code",
        "colab": {},
        "outputId": "68444f91-cd5c-4431-cd86-f6e480a42865"
      },
      "source": [
        "cv_df = pd.DataFrame(cv_array,columns=cv.get_feature_names()).add_prefix('Counts_')\n",
        "speech_df = pd.concat([speech_df,cv_df],axis=1, sort=False)\n",
        "speech_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Counts_year</th>\n",
              "      <th>Counts_years</th>\n",
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              "      <th>Counts_you</th>\n",
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              "      <th>Counts_your</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>First Inaugural Address</td>\n",
              "      <td>Thursday, April 30, 1789</td>\n",
              "      <td>Fellow-Citizens of the Senate and of the House...</td>\n",
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              "      <th>1</th>\n",
              "      <td>George Washington</td>\n",
              "      <td>Second Inaugural Address</td>\n",
              "      <td>Monday, March 4, 1793</td>\n",
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              "      <th>2</th>\n",
              "      <td>John Adams</td>\n",
              "      <td>Inaugural Address</td>\n",
              "      <td>Saturday, March 4, 1797</td>\n",
              "      <td>WHEN it was first perceived, in early times, t...</td>\n",
              "      <td>when it was first perceived  in early times  t...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Thomas Jefferson</td>\n",
              "      <td>First Inaugural Address</td>\n",
              "      <td>Wednesday, March 4, 1801</td>\n",
              "      <td>Friends and Fellow-Citizens:  CALLED upon to u...</td>\n",
              "      <td>friends and fellow citizens   called upon to u...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thomas Jefferson</td>\n",
              "      <td>Second Inaugural Address</td>\n",
              "      <td>Monday, March 4, 1805</td>\n",
              "      <td>PROCEEDING, fellow-citizens, to that qualifica...</td>\n",
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              "      <td>4</td>\n",
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              "<p>5 rows × 826 columns</p>\n",
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            ],
            "text/plain": [
              "                Name         Inaugural Address                      Date  \\\n",
              "0  George Washington   First Inaugural Address  Thursday, April 30, 1789   \n",
              "1  George Washington  Second Inaugural Address     Monday, March 4, 1793   \n",
              "2         John Adams         Inaugural Address   Saturday, March 4, 1797   \n",
              "3   Thomas Jefferson   First Inaugural Address  Wednesday, March 4, 1801   \n",
              "4   Thomas Jefferson  Second Inaugural Address     Monday, March 4, 1805   \n",
              "\n",
              "                                                text  \\\n",
              "0  Fellow-Citizens of the Senate and of the House...   \n",
              "1  Fellow Citizens:  I AM again called upon by th...   \n",
              "2  WHEN it was first perceived, in early times, t...   \n",
              "3  Friends and Fellow-Citizens:  CALLED upon to u...   \n",
              "4  PROCEEDING, fellow-citizens, to that qualifica...   \n",
              "\n",
              "                                          text_clean  char_cnt  word_cnt  \\\n",
              "0  fellow citizens of the senate and of the house...      8616      1432   \n",
              "1  fellow citizens   i am again called upon by th...       787       135   \n",
              "2  when it was first perceived  in early times  t...     13871      2323   \n",
              "3  friends and fellow citizens   called upon to u...     10144      1736   \n",
              "4  proceeding  fellow citizens  to that qualifica...     12902      2169   \n",
              "\n",
              "   avg_word_length  Counts_abiding  Counts_ability  ...  Counts_women  \\\n",
              "0         6.016760               0               0  ...             0   \n",
              "1         5.829630               0               0  ...             0   \n",
              "2         5.971158               0               0  ...             0   \n",
              "3         5.843318               0               0  ...             0   \n",
              "4         5.948363               0               0  ...             0   \n",
              "\n",
              "   Counts_words  Counts_work  Counts_wrong  Counts_year  Counts_years  \\\n",
              "0             0            0             0            0             1   \n",
              "1             0            0             0            0             0   \n",
              "2             0            0             0            2             3   \n",
              "3             0            1             2            0             0   \n",
              "4             0            0             0            2             2   \n",
              "\n",
              "   Counts_yet  Counts_you  Counts_young  Counts_your  \n",
              "0           0           5             0            9  \n",
              "1           0           0             0            1  \n",
              "2           0           0             0            1  \n",
              "3           2           7             0            7  \n",
              "4           2           4             0            4  \n",
              "\n",
              "[5 rows x 826 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Rpo0ek00-dsr"
      },
      "source": [
        "---\n",
        "### Tf-idf\n",
        "---\n",
        "While counts of occurrences of words can be useful to build models, words that occur many times may skew the results undesirably. To limit these common words from overpowering your model a form of normalization can be used. In this lesson we will be using **Term frequency-inverse document frequency** (**Tf-idf**). Tf-idf has the effect of reducing the value of common words, while increasing the weight of words that do not occur in many documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "c29xSq9Q-mcH"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Import `TfidfVectorizer` from `sklearn.feature_extraction.text`.\n",
        "* Instantiate `TfidfVectorizer` while limiting the number of features to 100 and removing English stop words.\n",
        "* Fit and apply the vectorizer on `text_clean` column in one step.\n",
        "* Create a DataFrame `tv_df` containing the weights of the words and the feature names as the column names.\n",
        "* Add the prefix `TFIDF_` to the column names for ease of identification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-F0vdfx9-yIq",
        "colab": {}
      },
      "source": [
        "# Import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Instantiate TfidfVectorizer\n",
        "tv = TfidfVectorizer(max_features=100, stop_words='english')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrRKZ3hbbw_-",
        "colab_type": "code",
        "colab": {},
        "outputId": "8225df3d-f49f-4c01-b227-388616470753"
      },
      "source": [
        "tf_trans = tv.fit_transform(speech_df['text_clean'])\n",
        "tv_df = pd.DataFrame(tf_trans.toarray(),columns=tv.get_feature_names()).add_prefix('TFIDF_')\n",
        "tv_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TFIDF_action</th>\n",
              "      <th>TFIDF_administration</th>\n",
              "      <th>TFIDF_america</th>\n",
              "      <th>TFIDF_american</th>\n",
              "      <th>TFIDF_americans</th>\n",
              "      <th>TFIDF_believe</th>\n",
              "      <th>TFIDF_best</th>\n",
              "      <th>TFIDF_better</th>\n",
              "      <th>TFIDF_change</th>\n",
              "      <th>TFIDF_citizens</th>\n",
              "      <th>...</th>\n",
              "      <th>TFIDF_things</th>\n",
              "      <th>TFIDF_time</th>\n",
              "      <th>TFIDF_today</th>\n",
              "      <th>TFIDF_union</th>\n",
              "      <th>TFIDF_united</th>\n",
              "      <th>TFIDF_war</th>\n",
              "      <th>TFIDF_way</th>\n",
              "      <th>TFIDF_work</th>\n",
              "      <th>TFIDF_world</th>\n",
              "      <th>TFIDF_years</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.133415</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.105388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.229644</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.045929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.136012</td>\n",
              "      <td>0.203593</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.060755</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.045929</td>\n",
              "      <td>0.052694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.261016</td>\n",
              "      <td>0.266097</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.179712</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.199157</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.092436</td>\n",
              "      <td>0.157058</td>\n",
              "      <td>0.073018</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026112</td>\n",
              "      <td>0.060460</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106072</td>\n",
              "      <td>...</td>\n",
              "      <td>0.032030</td>\n",
              "      <td>0.021214</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.062823</td>\n",
              "      <td>0.070529</td>\n",
              "      <td>0.024339</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063643</td>\n",
              "      <td>0.073018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.092693</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.090942</td>\n",
              "      <td>0.117831</td>\n",
              "      <td>0.045471</td>\n",
              "      <td>0.053335</td>\n",
              "      <td>0.223369</td>\n",
              "      <td>...</td>\n",
              "      <td>0.048179</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.094497</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.036610</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.039277</td>\n",
              "      <td>0.095729</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.041334</td>\n",
              "      <td>0.039761</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.031408</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.067393</td>\n",
              "      <td>0.039011</td>\n",
              "      <td>0.091514</td>\n",
              "      <td>0.273760</td>\n",
              "      <td>...</td>\n",
              "      <td>0.082667</td>\n",
              "      <td>0.164256</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.121605</td>\n",
              "      <td>0.030338</td>\n",
              "      <td>0.094225</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.054752</td>\n",
              "      <td>0.062817</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   TFIDF_action  TFIDF_administration  TFIDF_america  TFIDF_american  \\\n",
              "0      0.000000              0.133415       0.000000        0.105388   \n",
              "1      0.000000              0.261016       0.266097        0.000000   \n",
              "2      0.000000              0.092436       0.157058        0.073018   \n",
              "3      0.000000              0.092693       0.000000        0.000000   \n",
              "4      0.041334              0.039761       0.000000        0.031408   \n",
              "\n",
              "   TFIDF_americans  TFIDF_believe  TFIDF_best  TFIDF_better  TFIDF_change  \\\n",
              "0              0.0       0.000000    0.000000      0.000000      0.000000   \n",
              "1              0.0       0.000000    0.000000      0.000000      0.000000   \n",
              "2              0.0       0.000000    0.026112      0.060460      0.000000   \n",
              "3              0.0       0.090942    0.117831      0.045471      0.053335   \n",
              "4              0.0       0.000000    0.067393      0.039011      0.091514   \n",
              "\n",
              "   TFIDF_citizens  ...  TFIDF_things  TFIDF_time  TFIDF_today  TFIDF_union  \\\n",
              "0        0.229644  ...      0.000000    0.045929          0.0     0.136012   \n",
              "1        0.179712  ...      0.000000    0.000000          0.0     0.000000   \n",
              "2        0.106072  ...      0.032030    0.021214          0.0     0.062823   \n",
              "3        0.223369  ...      0.048179    0.000000          0.0     0.094497   \n",
              "4        0.273760  ...      0.082667    0.164256          0.0     0.121605   \n",
              "\n",
              "   TFIDF_united  TFIDF_war  TFIDF_way  TFIDF_work  TFIDF_world  TFIDF_years  \n",
              "0      0.203593   0.000000   0.060755    0.000000     0.045929     0.052694  \n",
              "1      0.199157   0.000000   0.000000    0.000000     0.000000     0.000000  \n",
              "2      0.070529   0.024339   0.000000    0.000000     0.063643     0.073018  \n",
              "3      0.000000   0.036610   0.000000    0.039277     0.095729     0.000000  \n",
              "4      0.030338   0.094225   0.000000    0.000000     0.054752     0.062817  \n",
              "\n",
              "[5 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qzcGgNmS_Yc5"
      },
      "source": [
        "---\n",
        "### Inspecting Tf-idf values\n",
        "---\n",
        "After creating Tf-idf features we will often want to understand what are the most highest scored words for each corpus. This can be achieved by isolating the row we want to examine and then sorting the the scores from high to low."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MP0QjLZJ_cvX"
      },
      "source": [
        "* Assign the first row of `tv_df` to `sample_row`.\n",
        "* `sample_row` is now a series of weights assigned to words. Sort these values to print the top 5 highest-rated words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g800sJg2_j2x",
        "colab": {},
        "outputId": "1350a00e-caab-43b8-9666-a4bca8ff52e4"
      },
      "source": [
        "# Isolate the row to be examined\n",
        "sample_row = tv_df.iloc[0]\n",
        "\n",
        "print(sample_row.sort_values(ascending=False).head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TFIDF_government    0.367430\n",
            "TFIDF_public        0.333237\n",
            "TFIDF_present       0.315182\n",
            "TFIDF_duty          0.238637\n",
            "TFIDF_citizens      0.229644\n",
            "Name: 0, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "A7WK0CHgH_3J"
      },
      "source": [
        "---\n",
        "### Sentiment analysis\n",
        "---\n",
        "You have been given the tweets about US airlines. Making use of this data, your task is to predict whether a tweet contains \n",
        "\n",
        "* positive, \n",
        "* negative, or \n",
        "* neutral sentiment \n",
        "\n",
        "about the airline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mupsP8kXIFld",
        "outputId": "6e45ae9b-bbca-47c0-c351-ae719400989b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "data_source_url = \"https://raw.githubusercontent.com/shala2020/shala2020.github.io/master/Lecture_Materials/Assignments/MachineLearning/L6/Tweets.csv\"\n",
        "airline_tweets = pd.read_csv(data_source_url)\n",
        "airline_tweets.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>airline_sentiment</th>\n",
              "      <th>airline_sentiment_confidence</th>\n",
              "      <th>negativereason</th>\n",
              "      <th>negativereason_confidence</th>\n",
              "      <th>airline</th>\n",
              "      <th>airline_sentiment_gold</th>\n",
              "      <th>name</th>\n",
              "      <th>negativereason_gold</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>text</th>\n",
              "      <th>tweet_coord</th>\n",
              "      <th>tweet_created</th>\n",
              "      <th>tweet_location</th>\n",
              "      <th>user_timezone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>570306133677760513</td>\n",
              "      <td>neutral</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>cairdin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica What @dhepburn said.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:35:52 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Eastern Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>570301130888122368</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:59 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>570301083672813571</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.6837</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yvonnalynn</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:48 -0800</td>\n",
              "      <td>Lets Play</td>\n",
              "      <td>Central Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>570301031407624196</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Bad Flight</td>\n",
              "      <td>0.7033</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:15:36 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>570300817074462722</td>\n",
              "      <td>negative</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Can't Tell</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>Virgin America</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jnardino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-02-24 11:14:45 -0800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pacific Time (US &amp; Canada)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
              "0  570306133677760513           neutral                        1.0000   \n",
              "1  570301130888122368          positive                        0.3486   \n",
              "2  570301083672813571           neutral                        0.6837   \n",
              "3  570301031407624196          negative                        1.0000   \n",
              "4  570300817074462722          negative                        1.0000   \n",
              "\n",
              "  negativereason  negativereason_confidence         airline  \\\n",
              "0            NaN                        NaN  Virgin America   \n",
              "1            NaN                     0.0000  Virgin America   \n",
              "2            NaN                        NaN  Virgin America   \n",
              "3     Bad Flight                     0.7033  Virgin America   \n",
              "4     Can't Tell                     1.0000  Virgin America   \n",
              "\n",
              "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
              "0                    NaN     cairdin                 NaN              0   \n",
              "1                    NaN    jnardino                 NaN              0   \n",
              "2                    NaN  yvonnalynn                 NaN              0   \n",
              "3                    NaN    jnardino                 NaN              0   \n",
              "4                    NaN    jnardino                 NaN              0   \n",
              "\n",
              "                                                text tweet_coord  \\\n",
              "0                @VirginAmerica What @dhepburn said.         NaN   \n",
              "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
              "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
              "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
              "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
              "\n",
              "               tweet_created tweet_location               user_timezone  \n",
              "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
              "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
              "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
              "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
              "4  2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AYlKJmRzIHlu"
      },
      "source": [
        "Instructions:\n",
        "\n",
        "* Draw a pie chart to see the number of tweets for each airline. \n",
        "* Draw a pie chart to see the distribution of sentiments across all the tweets. \n",
        "* Apply suitable data pre-processing steps to get rid of undesired symbols. This part has been done upto some extent.  \n",
        "* Using `TfidfVectorizer` class, convert text features into TF-IDF feature vectors.\n",
        "* `airline_sentiment` is the label and `text` is the feature. Apply suitable `train_test_split`, implement suitable machine learning classifier, and show the accuracy. \n",
        "\n",
        "Note: \n",
        "\n",
        "* For your convenience, the pre-processing of features have been done. \n",
        "* You are supposed to apply the vectorization on `processed_features` and implement ML algorithm. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NlRKrgYbxAO",
        "colab_type": "code",
        "colab": {},
        "outputId": "c82222ec-c7f3-4eb2-9173-769ca6b0563a"
      },
      "source": [
        "airline_tweets.airline.value_counts().plot(kind='pie',autopct='%1.1f');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRCS93fZbxAS",
        "colab_type": "code",
        "colab": {},
        "outputId": "6191471f-6e73-405b-fa2d-44b9d0590696"
      },
      "source": [
        "airline_tweets.\tairline_sentiment.value_counts().plot(kind='pie',autopct='%1.1f');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g5W-u3BCBfRN",
        "colab": {}
      },
      "source": [
        "# Extracting features and label\n",
        "features = airline_tweets.iloc[:, 10].values\n",
        "labels = airline_tweets.iloc[:, 1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgjbGf5BbxAa",
        "colab_type": "code",
        "colab": {},
        "outputId": "3e534f22-8319-458e-d498-03ffd00d538d"
      },
      "source": [
        "labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['neutral', 'positive', 'neutral', ..., 'neutral', 'negative',\n",
              "       'neutral'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oeDEmJ8yBiQj",
        "colab": {}
      },
      "source": [
        "import re \n",
        "\n",
        "processed_features = []\n",
        "\n",
        "for sentence in range(0, len(features)):\n",
        "    # Remove all the special characters\n",
        "    processed_feature = re.sub(r'\\W', ' ', str(features[sentence]))\n",
        "\n",
        "    # remove all single characters\n",
        "    processed_feature= re.sub(r'\\s+[a-zA-Z]\\s+', ' ', processed_feature)\n",
        "\n",
        "    # Remove single characters from the start\n",
        "    processed_feature = re.sub(r'\\^[a-zA-Z]\\s+', ' ', processed_feature) \n",
        "\n",
        "    # Substituting multiple spaces with single space\n",
        "    processed_feature = re.sub(r'\\s+', ' ', processed_feature, flags=re.I)\n",
        "\n",
        "    # Removing prefixed 'b'\n",
        "    processed_feature = re.sub(r'^b\\s+', '', processed_feature)\n",
        "\n",
        "    # Converting to Lowercase\n",
        "    processed_feature = processed_feature.lower()\n",
        "\n",
        "    processed_features.append(processed_feature)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWB2nKfVbxAh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_KCBbWGbxAk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfv = TfidfVectorizer(max_features=2500, min_df=8 , max_df= 0.8, stop_words = stopwords.words('english'))\n",
        "\n",
        "vectorised = tfv.fit_transform(processed_features).toarray()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jL45H-JKbxAn",
        "colab_type": "code",
        "colab": {},
        "outputId": "a68727b6-cfd2-40b5-ed3a-32c5380f6b93"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(vectorised, labels, test_size=0.2, random_state=0, stratify = labels)\n",
        "\n",
        "print(X_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11712, 2080)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4ZqT0WNbxAy",
        "colab_type": "code",
        "colab": {},
        "outputId": "a0704264-27ba-4fcd-bffa-0e8a2e63da55"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zea3UKKUbxA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "model = RandomForestClassifier(n_estimators=200, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1fh9w3nbxA7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit_and_test(model,X,X_t,y,y_t):\n",
        "    model.fit(X,y)\n",
        "    predictions = model.predict(X)\n",
        "    accuracy = accuracy_score(y, predictions)\n",
        "    print('Train accuracy:',np.round(accuracy*100, 2), '%')\n",
        "    predictions = model.predict(X_t)\n",
        "    accuracy = accuracy_score(y_t, predictions)\n",
        "    print('Test accuracy:',np.round(accuracy*100, 2), '%')\n",
        "    print(\"Confusion Matrix:\\n\")\n",
        "    print(confusion_matrix(y_test, predictions))\n",
        "    print(\"=\"*120)\n",
        "    print(\"Classification report:\",classification_report(y_test, predictions))\n",
        "    print(\"=\"*120)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_N1WpHPbxA-",
        "colab_type": "code",
        "colab": {},
        "outputId": "49ce2327-4e77-4b3d-ad9d-83a6ee9500b7"
      },
      "source": [
        "model = fit_and_test(model,X_train, X_test, y_train, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train accuracy: 99.27 %\n",
            "Test accuracy: 74.59 %\n",
            "Confusion Matrix:\n",
            "\n",
            "[[1712   91   33]\n",
            " [ 345  222   53]\n",
            " [ 152   70  250]]\n",
            "========================================================================================================================\n",
            "Classification report:               precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.78      0.93      0.85      1836\n",
            "     neutral       0.58      0.36      0.44       620\n",
            "    positive       0.74      0.53      0.62       472\n",
            "\n",
            "    accuracy                           0.75      2928\n",
            "   macro avg       0.70      0.61      0.64      2928\n",
            "weighted avg       0.73      0.75      0.72      2928\n",
            "\n",
            "========================================================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrdfR9BAbxBB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "columns = list(tfv.get_feature_names())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "AGtYTE7gbxBE",
        "colab_type": "code",
        "colab": {},
        "outputId": "2687beca-a64c-4259-9204-950c5cfe483f"
      },
      "source": [
        "# get importance\n",
        "importance = list(model.feature_importances_)\n",
        "# summarize feature importance\n",
        "feat_importances = pd.Series(model.feature_importances_, index=columns)\n",
        "feat_importances.nlargest(25).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ha8X2xiXbxBI",
        "colab_type": "text"
      },
      "source": [
        "Words like Thank great delayed cancelled hold love have great amazing best good importance in classifying the sentiments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ueany2DbxBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}