FinalProject_OverallPredicts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dependencies \n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical\n",
    "from keras import utils as np_utils\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "# Clean and Preprocess Data for Machine Learning\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Graphing dependencies \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scikitplot as skplt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import and create DataFrames\n",
    "race_stats = pd.read_csv('Stats_Races.csv')\n",
    "horse_stats = pd.read_csv('Stats_Horses.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_stats_df = pd.DataFrame(race_stats) \n",
    "horse_stats_df = pd.DataFrame(horse_stats) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>collected_at</th>\n",
       "      <th>market_id</th>\n",
       "      <th>position</th>\n",
       "      <th>place_paid</th>\n",
       "      <th>margin</th>\n",
       "      <th>horse_id</th>\n",
       "      <th>trainer_id</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>handicap_weight</th>\n",
       "      <th>...</th>\n",
       "      <th>tip_seven_place</th>\n",
       "      <th>tip_eight_win</th>\n",
       "      <th>tip_eight_place</th>\n",
       "      <th>tip_nine_win</th>\n",
       "      <th>tip_nine_place</th>\n",
       "      <th>age</th>\n",
       "      <th>sex_id</th>\n",
       "      <th>sire_id</th>\n",
       "      <th>dam_id</th>\n",
       "      <th>prize_money</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2016-06-27 02:54:32.082518</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>13.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2016-06-27 02:54:31.91754</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>...</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19290.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2016-06-27 02:54:32.121807</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-06-27 02:54:31.894908</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>...</td>\n",
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       "      <td>f</td>\n",
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       "      <td>f</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1215.0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                collected_at  market_id  position  place_paid  margin  \\\n",
       "0  13  2016-06-27 02:54:32.082518          1       NaN           0     NaN   \n",
       "1   8   2016-06-27 02:54:31.91754          1       NaN           0     NaN   \n",
       "2  14  2016-06-27 02:54:32.121807          1       NaN           0     NaN   \n",
       "3   7  2016-06-27 02:54:31.894908          1       NaN           0     NaN   \n",
       "4   3  2016-06-27 02:54:31.781492          1       NaN           0     NaN   \n",
       "\n",
       "   horse_id  trainer_id  rider_id  handicap_weight  ...  tip_seven_place  \\\n",
       "0        13        12.0      13.0             56.5  ...                t   \n",
       "1         8         8.0       8.0             56.5  ...                f   \n",
       "2        14        13.0      14.0             56.5  ...                f   \n",
       "3         7         7.0       7.0             56.5  ...                t   \n",
       "4         3         3.0       3.0             58.5  ...                f   \n",
       "\n",
       "   tip_eight_win tip_eight_place tip_nine_win  tip_nine_place  age  sex_id  \\\n",
       "0              t               t            f               t  6.0     3.0   \n",
       "1              f               f            f               f  6.0     3.0   \n",
       "2              f               f            f               f  3.0     2.0   \n",
       "3              f               t            f               t  3.0     2.0   \n",
       "4              f               f            f               f  4.0     1.0   \n",
       "\n",
       "  sire_id dam_id prize_money  \n",
       "0    13.0   13.0     16885.0  \n",
       "1     8.0    8.0     19290.0  \n",
       "2    14.0   14.0       575.0  \n",
       "3     7.0    7.0      1215.0  \n",
       "4     3.0    3.0      1175.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge CSV's to create one dataframe \n",
    "#merge_df = pd.merge(race_stats_df, horse_stats_df, how='left', left_on=['id'], right_on = ['id'])\n",
    "merge_df = pd.merge(race_stats_df, horse_stats_df, on=['id'])\n",
    "merge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>age</th>\n",
       "      <th>sex_id</th>\n",
       "      <th>prize_money</th>\n",
       "      <th>trainer_id</th>\n",
       "      <th>rider_id</th>\n",
       "      <th>sire_id</th>\n",
       "      <th>dam_id</th>\n",
       "      <th>handicap_weight</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8885.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13850.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28560.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     position  age  sex_id  prize_money  trainer_id  rider_id  sire_id  \\\n",
       "195       1.0  4.0     3.0       8885.0       177.0     105.0    233.0   \n",
       "196       2.0  4.0     3.0      13850.0       146.0      87.0     18.0   \n",
       "197       3.0  6.0     1.0      28560.0       251.0     101.0    147.0   \n",
       "198       4.0  4.0     3.0        550.0       177.0      90.0      3.0   \n",
       "199       5.0  3.0     2.0       1700.0       148.0     110.0     92.0   \n",
       "\n",
       "     dam_id  handicap_weight  \n",
       "195   439.0             57.0  \n",
       "196   436.0             60.5  \n",
       "197   441.0             55.5  \n",
       "198   440.0             56.0  \n",
       "199   437.0             59.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create new dataframe with only the desired columns and drop na  \n",
    "new_df = merge_df[['position', 'age', 'sex_id', 'prize_money', 'trainer_id', 'rider_id', 'sire_id', 'dam_id', 'handicap_weight']].dropna()\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ax = sns.catplot(x='prize_money',kind='count',data=new_df,orient=\"h\")\n",
    "#ax.fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"age\",y=\"prize_money\",data=new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = new_df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df[['age', 'sex_id', 'prize_money', 'trainer_id', 'rider_id', 'sire_id', 'dam_id', 'handicap_weight']]\n",
    "y = new_df[[\"position\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9592, 8) (9592, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScater model and fit it to the training data\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "y_train_scaled = y_scaler.transform(y_train)\n",
    "y_test_scaled = y_scaler.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=200)\n",
    "rf = rf.fit(X, y)\n",
    "rf.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.16232834737702623, 'dam_id'),\n",
       " (0.1597577354512162, 'sire_id'),\n",
       " (0.1593484881863416, 'trainer_id'),\n",
       " (0.15559803060671368, 'rider_id'),\n",
       " (0.15257362004648717, 'prize_money'),\n",
       " (0.0923551339580165, 'handicap_weight'),\n",
       " (0.07654270789061221, 'age'),\n",
       " (0.04149593648358647, 'sex_id')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = ['age', 'sex_id', 'prize_money', 'trainer_id', 'rider_id', 'sire_id', 'dam_id', 'handicap_weight']\n",
    "sorted(zip(rf.feature_importances_, feature_names), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/preprocessing/label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "lab_enc = preprocessing.LabelEncoder()\n",
    "y_train_encoded = lab_enc.fit_transform(y_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "lab_enc = preprocessing.LabelEncoder()\n",
    "y_test_encoded = lab_enc.fit_transform(y_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train_scaled, y_train_encoded)\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.11036975257158743\n",
      "Testing Data Score: 0.10133444537114263\n"
     ]
    }
   ],
   "source": [
    "#Validate\n",
    "print(f\"Training Data Score: {classifier.score(X_train_scaled, y_train_encoded)}\")\n",
    "print(f\"Testing Data Score: {classifier.score(X_test_scaled, y_test_encoded)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Predictions: [ 0  0  0  0  0 15  0  0  0  0]\n",
      "First 10 Actual labels: [[5.0], [3.0], [10.0], [2.0], [7.0], [6.0], [9.0], [1.0], [3.0], [1.0]]\n"
     ]
    }
   ],
   "source": [
    "#Predict\n",
    "predictions = classifier.predict(X_test)\n",
    "print(f\"First 10 Predictions: {predictions[:10]}\")\n",
    "print(f\"First 10 Actual labels: {y_test[:10].values.tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape\n",
    "y_r = y_test.values.reshape((2398))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2393</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2394</td>\n",
       "      <td>15</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2395</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2396</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2397</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2398 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prediction  Actual\n",
       "0              0     5.0\n",
       "1              0     3.0\n",
       "2              0    10.0\n",
       "3              0     2.0\n",
       "4              0     7.0\n",
       "...          ...     ...\n",
       "2393          15    10.0\n",
       "2394          15     6.0\n",
       "2395           0    12.0\n",
       "2396           4     1.0\n",
       "2397          15     4.0\n",
       "\n",
       "[2398 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe\n",
    "prediction_df = pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_r})\n",
    "prediction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "//anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = classifier.predict(X_test_scaled)\n",
    "classifier.fit(X_train_scaled, y_train_encoded)\n",
    "plt.scatter(classifier.predict(X_train_scaled), classifier.predict(X_train_scaled) - y_train_encoded, c=\"blue\", label=\"Training Data\")\n",
    "plt.scatter(classifier.predict(X_test_scaled), classifier.predict(X_test_scaled) - y_test_encoded, c=\"orange\", label=\"Testing Data\")\n",
    "plt.legend()\n",
    "plt.hlines(y=0, xmin=y_test_encoded.min(), xmax=y_test_encoded.max())\n",
    "plt.title(\"Residual Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}