multiclass-prediction1-2.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":60893,"databundleVersionId":7000181,"sourceType":"competition"}],"dockerImageVersionId":30626,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-01T17:51:45.707769Z","iopub.execute_input":"2024-01-01T17:51:45.708217Z","iopub.status.idle":"2024-01-01T17:51:46.173910Z","shell.execute_reply.started":"2024-01-01T17:51:45.708167Z","shell.execute_reply":"2024-01-01T17:51:46.172600Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nimport xgboost as xgb\nimport lightgbm as lgb\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import MinMaxScaler\nfrom scipy import stats\nfrom sklearn.cluster import DBSCAN\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:21:23.621112Z","iopub.execute_input":"2024-01-01T18:21:23.621704Z","iopub.status.idle":"2024-01-01T18:21:38.203772Z","shell.execute_reply.started":"2024-01-01T18:21:23.621651Z","shell.execute_reply":"2024-01-01T18:21:38.202319Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1.Read dataset","metadata":{}},{"cell_type":"markdown","source":"#Read the test,train and submission file into python environment.","metadata":{}},{"cell_type":"code","source":"df_train=pd.read_csv(\"/kaggle/input/playground-series-s3e26/train.csv\")\ndf_test=pd.read_csv(\"/kaggle/input/playground-series-s3e26/test.csv\")\nresult=pd.read_csv(\"/kaggle/input/playground-series-s3e26/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:07:46.050396Z","iopub.execute_input":"2024-01-01T18:07:46.050847Z","iopub.status.idle":"2024-01-01T18:07:46.116639Z","shell.execute_reply.started":"2024-01-01T18:07:46.050812Z","shell.execute_reply":"2024-01-01T18:07:46.115408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Display information about train dataset","metadata":{}},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:07:53.690891Z","iopub.execute_input":"2024-01-01T18:07:53.691882Z","iopub.status.idle":"2024-01-01T18:07:53.711548Z","shell.execute_reply.started":"2024-01-01T18:07:53.691831Z","shell.execute_reply":"2024-01-01T18:07:53.710433Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:00.378763Z","iopub.execute_input":"2024-01-01T18:08:00.379970Z","iopub.status.idle":"2024-01-01T18:08:00.409464Z","shell.execute_reply.started":"2024-01-01T18:08:00.379926Z","shell.execute_reply":"2024-01-01T18:08:00.408256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:08.720946Z","iopub.execute_input":"2024-01-01T18:08:08.722022Z","iopub.status.idle":"2024-01-01T18:08:08.729051Z","shell.execute_reply.started":"2024-01-01T18:08:08.721977Z","shell.execute_reply":"2024-01-01T18:08:08.727841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Display information about testdataset","metadata":{}},{"cell_type":"code","source":"df_test.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:13.900958Z","iopub.execute_input":"2024-01-01T18:08:13.901421Z","iopub.status.idle":"2024-01-01T18:08:13.922645Z","shell.execute_reply.started":"2024-01-01T18:08:13.901383Z","shell.execute_reply":"2024-01-01T18:08:13.921296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:18.840628Z","iopub.execute_input":"2024-01-01T18:08:18.841713Z","iopub.status.idle":"2024-01-01T18:08:18.869871Z","shell.execute_reply.started":"2024-01-01T18:08:18.841675Z","shell.execute_reply":"2024-01-01T18:08:18.868629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-01T17:52:58.250687Z","iopub.execute_input":"2024-01-01T17:52:58.251112Z","iopub.status.idle":"2024-01-01T17:52:58.258357Z","shell.execute_reply.started":"2024-01-01T17:52:58.251077Z","shell.execute_reply":"2024-01-01T17:52:58.257126Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The following steps are the sequence of data preprocessing steps.\n\nHandling Missing Values: Check for and handle any missing values in your dataset.\n\nOutlier Detection and Treatment: Identify and handle outliers in the data. Outliers can significantly impact the performance of certain scaling techniques, so it's often a good idea to address them early in the preprocessing pipeline.\n \nScaling: Scale numerical features if needed. Common scaling techniques include min-max scaling, standardization (z-score scaling), and robust scaling.\n\nEncoding Categorical Variables: Convert categorical variables into a numerical format if necessary, using techniques such as one-hot encoding or label encoding.\n\nFeature Engineering: Create new features or transform existing ones to better represent the underlying patterns in the data.\n\nSplitting Data: Split the data into training and testing sets for model evaluation.\n\nModel-Specific Preprocessing: Some machine learning models may have specific preprocessing requirements, so consider the needs of your chosen model.\n\n","metadata":{}},{"cell_type":"markdown","source":"# DATA EXPLORATION","metadata":{}},{"cell_type":"markdown","source":"**TRAIN DATASET EXPLORATION**","metadata":{}},{"cell_type":"code","source":"df_train.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T17:53:14.351314Z","iopub.execute_input":"2024-01-01T17:53:14.351842Z","iopub.status.idle":"2024-01-01T17:53:14.413724Z","shell.execute_reply.started":"2024-01-01T17:53:14.351762Z","shell.execute_reply":"2024-01-01T17:53:14.412523Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Function to convert two-digit year to four-digit year\ndef convert_to_four_digit_year(two_digit_year):\n    #if two_digit_year < 50:\n       # return 2000 + two_digit_year\n    #else:\n    birthYear=1900 + two_digit_year\n    Y_age=2020-birthYear#Suppose the data is collected on the 2020 year.\n    return   Y_age\nAge_l=[]\n# Process each numerical value in the dataset\nfor numeric_value in df_train['Age']:\n    # Extracting year, month, and day\n    #print(numeric_value)\n    Day=int(numeric_value / 1000)\n    numeric_value = numeric_value % 1000\n    month =int( numeric_value / 100)\n    numeric_value = numeric_value % 100\n    two_digit_year=numeric_value\n    #df['Year']=1900 + two_digit_year\n   # print(numeric_value)\n\n    # Convert two-digit year to four-digit year\n    four_digit_year= convert_to_four_digit_year(two_digit_year)\n    Age_l.append(four_digit_year)\n    # Convert the list to a DataFrame\nAgeu = pd.DataFrame({'Age Column': Age_l})\n\n# Concatenate the existing DataFrame and the new column DataFrame\ndf_train= pd.concat([df_train,Ageu], axis=1)\n    # Display the result\n    #print(f\"Original: {numeric_value}{two_digit_year:02d}, Converted: {day:02d}-{month:02d}-{four_digit_year}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:29.079320Z","iopub.execute_input":"2024-01-01T18:08:29.079709Z","iopub.status.idle":"2024-01-01T18:08:29.106910Z","shell.execute_reply.started":"2024-01-01T18:08:29.079680Z","shell.execute_reply":"2024-01-01T18:08:29.105577Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Drop the age from dataset.Already calculated the age column from age.So remove the age from the dataset.","metadata":{}},{"cell_type":"code","source":"df_train.drop('Age', axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:36.510759Z","iopub.execute_input":"2024-01-01T18:08:36.511156Z","iopub.status.idle":"2024-01-01T18:08:36.519581Z","shell.execute_reply.started":"2024-01-01T18:08:36.511127Z","shell.execute_reply":"2024-01-01T18:08:36.518222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  To display unique values in each column\nfor column in df_train.columns:\n    value_counts = df_train[column].nunique()\n    print(f\"Value counts for {column}:\\n{value_counts}\")\n    print(\"=\" * 30)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:42.562346Z","iopub.execute_input":"2024-01-01T18:08:42.563496Z","iopub.status.idle":"2024-01-01T18:08:42.582419Z","shell.execute_reply.started":"2024-01-01T18:08:42.563444Z","shell.execute_reply":"2024-01-01T18:08:42.581314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:51.120820Z","iopub.execute_input":"2024-01-01T18:08:51.121251Z","iopub.status.idle":"2024-01-01T18:08:51.186267Z","shell.execute_reply.started":"2024-01-01T18:08:51.121211Z","shell.execute_reply":"2024-01-01T18:08:51.184610Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:08:59.553358Z","iopub.execute_input":"2024-01-01T18:08:59.553751Z","iopub.status.idle":"2024-01-01T18:08:59.574094Z","shell.execute_reply.started":"2024-01-01T18:08:59.553722Z","shell.execute_reply":"2024-01-01T18:08:59.572689Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**OUTLIER DETECTION**","metadata":{}},{"cell_type":"markdown","source":"FOR NUMERICAL VALUES USING Z score","metadata":{}},{"cell_type":"code","source":"# Specify the Z-score threshold\nthreshold = 3\n\n# Loop through all numerical columns in the DataFrame\nfor column in df_train.select_dtypes(include=['float64', 'int64']).columns:\n    # Calculate Z-scores for the current column\n    z_scores = stats.zscore(df_train[column])\n    \n    # Identify outliers\n    outliers = (abs(z_scores) > threshold)\n    mean_value = df_train[column].mean()\n    #print(column)\n    print(\"Number of outliers detected\",column,outliers.sum())\n    # Replace outliers with the mean value\n    df_train.loc[outliers, column] = mean_value\n     #Handle outliers (replace with the mean value)\n    #df_train[column][outliers] = df_train[column].mean()\n\n# Print the number of outliers detected and handled\n#print(\"Number of outliers detected and handled:\", sum(outliers.sum() for outliers in outliers))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"CONVERT CATEGORICAL VALUE INTO NUMERICAL VALUE","metadata":{}},{"cell_type":"code","source":"# Get a list of categorical column names\ncategorical_columns = df_train.select_dtypes(include=['object']).columns\n# Initialize a dictionary to store label encoders\nlabel_encoders = {}\n\n# Apply label encoding using a loop\nfor column in categorical_columns:\n    label_encoder = LabelEncoder()\n    df_train[column] = label_encoder.fit_transform(df_train[column])\n    #label_encoders[column] = label_encoder\n# Display the result\nprint(df_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:09:30.500668Z","iopub.execute_input":"2024-01-01T18:09:30.501076Z","iopub.status.idle":"2024-01-01T18:09:30.523573Z","shell.execute_reply.started":"2024-01-01T18:09:30.501046Z","shell.execute_reply":"2024-01-01T18:09:30.521926Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\n# Initialize MinMaxScaler\nscaler = MinMaxScaler()\n\n# Apply Min-Max scaling to numerical columns\ndf_train_scaled = scaler.fit_transform(df_train)\n# Round the scaled data to two decimal places\nscaled_data_rounded = np.round(df_train_scaled, 2)\n\ndf_train_s = pd.DataFrame(scaled_data_rounded,columns=df_train.columns)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:14:47.270529Z","iopub.execute_input":"2024-01-01T18:14:47.270967Z","iopub.status.idle":"2024-01-01T18:14:47.286084Z","shell.execute_reply.started":"2024-01-01T18:14:47.270931Z","shell.execute_reply":"2024-01-01T18:14:47.284785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"# Set the 'id' column as the default index\ndf_train_s.set_index('id', inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:16:01.521409Z","iopub.execute_input":"2024-01-01T18:16:01.521848Z","iopub.status.idle":"2024-01-01T18:16:01.528271Z","shell.execute_reply.started":"2024-01-01T18:16:01.521810Z","shell.execute_reply":"2024-01-01T18:16:01.526676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train_s.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:14:53.951441Z","iopub.execute_input":"2024-01-01T18:14:53.951820Z","iopub.status.idle":"2024-01-01T18:14:53.992790Z","shell.execute_reply.started":"2024-01-01T18:14:53.951791Z","shell.execute_reply":"2024-01-01T18:14:53.991389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"BOXPLOT is used for data exploration","metadata":{}},{"cell_type":"code","source":"# Create a boxplot\nAge1='Age Column'\nplt.figure(figsize=(8, 6))\nplt.boxplot(df_train['Age Column'],)  # 'vert=False' for a horizontal boxplot\nplt.title(f'Boxplot for {Age1}')\nplt.xlabel(Age1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:16:25.730812Z","iopub.execute_input":"2024-01-01T18:16:25.731229Z","iopub.status.idle":"2024-01-01T18:16:26.037812Z","shell.execute_reply.started":"2024-01-01T18:16:25.731178Z","shell.execute_reply":"2024-01-01T18:16:26.036605Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a boxplot\nAge1='Bilirubin'\nplt.figure(figsize=(8, 6))\nplt.boxplot(df_train['Bilirubin'],)  # 'vert=False' for a horizontal boxplot\nplt.title(f'Boxplot for {Age1}')\nplt.xlabel(Age1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-31T18:11:49.973471Z","iopub.execute_input":"2023-12-31T18:11:49.974180Z","iopub.status.idle":"2023-12-31T18:11:50.180824Z","shell.execute_reply.started":"2023-12-31T18:11:49.974119Z","shell.execute_reply":"2023-12-31T18:11:50.179510Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a histogram\nplt.hist(df_train['Albumin'], bins=40, color='blue', alpha=0.7)\nplt.title('Histogram Example')\nplt.xlabel('Values')\nplt.ylabel('Frequency')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-31T18:11:53.524063Z","iopub.execute_input":"2023-12-31T18:11:53.524477Z","iopub.status.idle":"2023-12-31T18:11:53.871813Z","shell.execute_reply.started":"2023-12-31T18:11:53.524443Z","shell.execute_reply":"2023-12-31T18:11:53.870402Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Display the count of null values in each column\nnull_values = df_train.isnull().sum()\nnull_values","metadata":{"execution":{"iopub.status.busy":"2023-12-31T18:12:00.442816Z","iopub.execute_input":"2023-12-31T18:12:00.443179Z","iopub.status.idle":"2023-12-31T18:12:00.462095Z","shell.execute_reply.started":"2023-12-31T18:12:00.443151Z","shell.execute_reply":"2023-12-31T18:12:00.460463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#To get the count of Bilirubin having values more than normal values ","metadata":{}},{"cell_type":"code","source":"X = df_train_s.drop('Status', axis=1)\ny=df_train_s['Status']","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:18:20.232786Z","iopub.execute_input":"2024-01-01T18:18:20.233248Z","iopub.status.idle":"2024-01-01T18:18:20.241892Z","shell.execute_reply.started":"2024-01-01T18:18:20.233214Z","shell.execute_reply":"2024-01-01T18:18:20.240560Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:34:08.439596Z","iopub.execute_input":"2024-01-01T18:34:08.440010Z","iopub.status.idle":"2024-01-01T18:34:08.457867Z","shell.execute_reply.started":"2024-01-01T18:34:08.439974Z","shell.execute_reply":"2024-01-01T18:34:08.456687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:36:56.960801Z","iopub.execute_input":"2024-01-01T18:36:56.961207Z","iopub.status.idle":"2024-01-01T18:36:56.975324Z","shell.execute_reply.started":"2024-01-01T18:36:56.961164Z","shell.execute_reply":"2024-01-01T18:36:56.973713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = RandomForestRegressor()\n# Fit the pipeline on the training data\nmodel.fit(X_train,y_train)\n# Evaluate the model on the testing data\n#rf_predictions = pipeline.predict(X_test)\n#rf_accuracy = accuracy_score(y_test, rf_predictions)\n#score = model.score(rf_predictions, y_test)\nscore = model.score(X_test, y_test)\nprint(f'Model R2 Score: {score}')\n# Evaluate the model on the testing data\n#score = pipeline.score(X_test, y_test)\n#print(f'Model R2 Score: {score}')","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:38:04.099674Z","iopub.execute_input":"2024-01-01T18:38:04.100124Z","iopub.status.idle":"2024-01-01T18:38:07.487126Z","shell.execute_reply.started":"2024-01-01T18:38:04.100092Z","shell.execute_reply":"2024-01-01T18:38:07.485947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import preprocessing\n#convert y values to categorical values\nlab = preprocessing.LabelEncoder()\ny_test = lab.fit_transform(y_test)\ny_train = lab.fit_transform(y_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:48:01.761543Z","iopub.execute_input":"2024-01-01T18:48:01.762011Z","iopub.status.idle":"2024-01-01T18:48:01.770775Z","shell.execute_reply.started":"2024-01-01T18:48:01.761979Z","shell.execute_reply":"2024-01-01T18:48:01.769343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define a list of models\nmodels = [\n    ('Support Vector Machine', SVC()),\n    ('XGBoost', xgb.XGBClassifier()),\n    ('Decision Tree', DecisionTreeClassifier()),\n    ('Random Forest', RandomForestClassifier()),\n    ('LightGBM', lgb.LGBMClassifier())\n]\n\n# Iterate through the models and train each one\nfor model_name, model in models:\n    model.fit(X_train,y_train)\n    #pipeline.fit(X_train,y_train)\n    score = model.score(X_test, y_test)\n    print(f\"{model_name} score Accuracy: {score}\")\n    #model.fit(X_train, y_train)\n    predictions = model.predict(X_test)\n    accuracy = accuracy_score(y_test, predictions)\n    print(f\"{model_name} Accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:42:06.678775Z","iopub.execute_input":"2024-01-01T18:42:06.679251Z","iopub.status.idle":"2024-01-01T18:42:14.034324Z","shell.execute_reply.started":"2024-01-01T18:42:06.679215Z","shell.execute_reply":"2024-01-01T18:42:14.032367Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_name=\"LGBM Classifier\"\nmodel1=lgb.LGBMClassifier()\nmodel1.fit(X_train,y_train)\n    #pipeline.fit(X_train,y_train)\nscore = model1.score(X_test, y_test)\nprint(f\"{model_name} score Accuracy: {score}\")\n    #model.fit(X_train, y_train)\npredictions = model1.predict(X_test)\naccuracy = accuracy_score(y_test, predictions)\nprint(f\"{model_name} Accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:48:29.211870Z","iopub.execute_input":"2024-01-01T18:48:29.212489Z","iopub.status.idle":"2024-01-01T18:48:31.824917Z","shell.execute_reply.started":"2024-01-01T18:48:29.212412Z","shell.execute_reply":"2024-01-01T18:48:31.823999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**TEST DATSET EXPLORATION**","metadata":{}},{"cell_type":"code","source":"df_test.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:51:52.901089Z","iopub.execute_input":"2024-01-01T18:51:52.901522Z","iopub.status.idle":"2024-01-01T18:51:52.963934Z","shell.execute_reply.started":"2024-01-01T18:51:52.901491Z","shell.execute_reply":"2024-01-01T18:51:52.962638Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Function to convert two-digit year to four-digit year\ndef convert_to_four_digit_year(two_digit_year):\n    #if two_digit_year < 50:\n       # return 2000 + two_digit_year\n    #else:\n    birthYear=1900 + two_digit_year\n    Y_age=2020-birthYear#Suppose the data is collected on the 2020 year.\n    return   Y_age\nAge_l=[]\n# Process each numerical value in the dataset\nfor numeric_value in df_test['Age']:\n    # Extracting year, month, and day\n    #print(numeric_value)\n    Day=int(numeric_value / 1000)\n    numeric_value = numeric_value % 1000\n    month =int( numeric_value / 100)\n    numeric_value = numeric_value % 100\n    two_digit_year=numeric_value\n    #df['Year']=1900 + two_digit_year\n   # print(numeric_value)\n\n    # Convert two-digit year to four-digit year\n    four_digit_year= convert_to_four_digit_year(two_digit_year)\n    Age_l.append(four_digit_year)\n    # Convert the list to a DataFrame\nAgeu = pd.DataFrame({'Age Column': Age_l})\n\n# Concatenate the existing DataFrame and the new column DataFrame\ndf_test= pd.concat([df_test,Ageu], axis=1)\n    # Display the result\n    #print(f\"Original: {numeric_value}{two_digit_year:02d}, Converted: {day:02d}-{month:02d}-{four_digit_year}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:53:43.548365Z","iopub.execute_input":"2024-01-01T18:53:43.548917Z","iopub.status.idle":"2024-01-01T18:53:43.575926Z","shell.execute_reply.started":"2024-01-01T18:53:43.548882Z","shell.execute_reply":"2024-01-01T18:53:43.572270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:55:02.381865Z","iopub.execute_input":"2024-01-01T18:55:02.382328Z","iopub.status.idle":"2024-01-01T18:55:02.401606Z","shell.execute_reply.started":"2024-01-01T18:55:02.382294Z","shell.execute_reply":"2024-01-01T18:55:02.400295Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.drop('Age', axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:54:42.914616Z","iopub.execute_input":"2024-01-01T18:54:42.915095Z","iopub.status.idle":"2024-01-01T18:54:44.907469Z","shell.execute_reply.started":"2024-01-01T18:54:42.915063Z","shell.execute_reply":"2024-01-01T18:54:44.904080Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  To display unique values in each column\nfor column in df_test.columns:\n    value_counts = df_test[column].nunique()\n    print(f\"Value counts for {column}:\\n{value_counts}\")\n    print(\"=\" * 30)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:56:02.481607Z","iopub.execute_input":"2024-01-01T18:56:02.482048Z","iopub.status.idle":"2024-01-01T18:56:02.496156Z","shell.execute_reply.started":"2024-01-01T18:56:02.482018Z","shell.execute_reply":"2024-01-01T18:56:02.494984Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**OUTLIER DETECTION**","metadata":{}},{"cell_type":"code","source":"# Specify the Z-score threshold\nthreshold = 3\n\n# Loop through all numerical columns in the DataFrame\nfor column in df_test.select_dtypes(include=['float64', 'int64']).columns:\n    # Calculate Z-scores for the current column\n    z_scores = stats.zscore(df_test[column])\n    \n    # Identify outliers\n    outliers = (abs(z_scores) > threshold)\n    mean_value = df_test[column].mean()\n    #print(column)\n    print(\"Number of outliers detected\",column,outliers.sum())\n    # Replace outliers with the mean value\n    df_test.loc[outliers, column] = mean_value\n     #Handle outliers (replace with the mean value)\n    #df_train[column][outliers] = df_train[column].mean()\n\n# Print the number of outliers detected and handled","metadata":{"execution":{"iopub.status.busy":"2024-01-01T18:58:51.242814Z","iopub.execute_input":"2024-01-01T18:58:51.244095Z","iopub.status.idle":"2024-01-01T18:58:51.281233Z","shell.execute_reply.started":"2024-01-01T18:58:51.244045Z","shell.execute_reply":"2024-01-01T18:58:51.280063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get a list of categorical column names\ncategorical_columns = df_test.select_dtypes(include=['object']).columns\n# Initialize a dictionary to store label encoders\nlabel_encoders = {}\n\n# Apply label encoding using a loop\nfor column in categorical_columns:\n    label_encoder = LabelEncoder()\n    df_test[column] = label_encoder.fit_transform(df_test[column])\n    #label_encoders[column] = label_encoder\n# Display the result\nprint(df_test)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:00:40.019835Z","iopub.execute_input":"2024-01-01T19:00:40.020321Z","iopub.status.idle":"2024-01-01T19:00:40.061495Z","shell.execute_reply.started":"2024-01-01T19:00:40.020288Z","shell.execute_reply":"2024-01-01T19:00:40.060132Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\n# Initialize MinMaxScaler\nscaler = MinMaxScaler()\n\n# Apply Min-Max scaling to numerical columns\ndf_test_scaled = scaler.fit_transform(df_test)\n# Round the scaled data to two decimal places\nscaled_data_rounded = np.round(df_test_scaled, 2)\n\ndf_test_s = pd.DataFrame(scaled_data_rounded,columns=df_test.columns)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:02:01.710571Z","iopub.execute_input":"2024-01-01T19:02:01.711607Z","iopub.status.idle":"2024-01-01T19:02:01.725344Z","shell.execute_reply.started":"2024-01-01T19:02:01.711563Z","shell.execute_reply":"2024-01-01T19:02:01.723659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set the 'id' column as the default index\ndf_test_s.set_index('id', inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:02:59.961405Z","iopub.execute_input":"2024-01-01T19:02:59.961821Z","iopub.status.idle":"2024-01-01T19:02:59.968291Z","shell.execute_reply.started":"2024-01-01T19:02:59.961791Z","shell.execute_reply":"2024-01-01T19:02:59.967025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = model1.predict(df_test_s)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:04:46.109174Z","iopub.execute_input":"2024-01-01T19:04:46.109700Z","iopub.status.idle":"2024-01-01T19:04:46.162612Z","shell.execute_reply.started":"2024-01-01T19:04:46.109666Z","shell.execute_reply":"2024-01-01T19:04:46.161389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions1 = model1.predict_proba(df_test_s)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:05:55.929554Z","iopub.execute_input":"2024-01-01T19:05:55.930214Z","iopub.status.idle":"2024-01-01T19:05:55.978261Z","shell.execute_reply.started":"2024-01-01T19:05:55.930155Z","shell.execute_reply":"2024-01-01T19:05:55.977292Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final = pd.read_csv('/kaggle/input/playground-series-s3e26/sample_submission.csv')\nfinal.Status_C = predictions1[:, 0]\nfinal.Status_CL = predictions1[:, 1]\nfinal.Status_D = predictions1[:, 2]\nfinal.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:06:10.410616Z","iopub.execute_input":"2024-01-01T19:06:10.411036Z","iopub.status.idle":"2024-01-01T19:06:10.438787Z","shell.execute_reply.started":"2024-01-01T19:06:10.411005Z","shell.execute_reply":"2024-01-01T19:06:10.437287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# write to a file\nfinal.to_csv('./submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T19:06:30.110795Z","iopub.execute_input":"2024-01-01T19:06:30.111215Z","iopub.status.idle":"2024-01-01T19:06:30.167780Z","shell.execute_reply.started":"2024-01-01T19:06:30.111165Z","shell.execute_reply":"2024-01-01T19:06:30.166588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# REFERENCES\n1. https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/#:~:text=Normalization%20is%20a%20scaling%20technique,values%20of%20the%20feature%2C%20respectively.","metadata":{}},{"cell_type":"markdown","source":"2.https://www.analyticsvidhya.com/blog/2021/05/detecting-and-treating-outliers-treating-the-odd-one-out/\n3.https://www.sciencedirect.com/sdfe/pdf/download/eid/3-s2.0-B9780323428767000260/first-page-pdf","metadata":{}}]}