NUS_DATATHON_SINGLIFE_SoloY.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The cell below is for you to keep track of the libraries used and install those libraries quickly\n",
    "##### Ensure that the proper library names are used and the syntax of `%pip install PACKAGE_NAME` is followed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\alvin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.4.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.19.5 in c:\\users\\alvin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from scikit-learn) (1.25.2)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "#%pip install pandas \n",
    "#%pip install matplotlib\n",
    "%pip install scikit-learn\n",
    "%pip install seaborn\n",
    "%pip install imbalanced-learn\n",
    "%pip install ydata-profiling\n",
    "\n",
    "\n",
    "# add commented pip installation lines for packages used as shown above for ease of testing\n",
    "# the line should be of the format %pip install PACKAGE_NAME "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **DO NOT CHANGE** the filepath variable\n",
    "##### Instead, create a folder named 'data' in your current working directory and \n",
    "##### have the .parquet file inside that. A relative path *must* be used when loading data into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can have as many cells as you want for code\n",
    "import pandas as pd\n",
    "filepath = \"./data/catB_train.parquet\" \n",
    "# the initialised filepath MUST be a relative path to a folder named data that contains the parquet file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "df = pd.read_parquet(filepath, engine='pyarrow')\n",
    "\n",
    "def data_cleaning(df):\n",
    "    \n",
    "    #missing value\n",
    "    ##remove \n",
    "    thres = 75\n",
    "    for col in df.columns:\n",
    "        if col == 'f_purchase_lh':\n",
    "            continue;\n",
    "        percent = df[col].isna().sum()/df.shape[0]*100\n",
    "        if percent > thres:\n",
    "            df = df.drop(columns = col)\n",
    "    \n",
    "    ##impute\n",
    "    \n",
    "    numeric_cols = df.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
    "    numeric_cols = numeric_cols[numeric_cols !=  'f_purchase_lh']\n",
    "    df[numeric_cols] = df[numeric_cols].apply(lambda x: x.fillna(x.median()))\n",
    "    \n",
    "    object_imputer = SimpleImputer(missing_values = None, strategy='most_frequent')\n",
    "    object_cols_with_missing = df.columns[df.isnull().any() & (df.columns != 'f_purchase_lh')]\n",
    "    df[object_cols_with_missing] = object_imputer.fit_transform(df[object_cols_with_missing])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "def encode(df):\n",
    "\n",
    "    #ordinal encode\n",
    "    object_columns = df.select_dtypes('object').columns\n",
    "    ordinal_encoder = OrdinalEncoder()\n",
    "    for col in object_columns:\n",
    "         df[col] = ordinal_encoder.fit_transform(df[[col]])\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **ALL** Code for machine learning and dataset analysis should be entered below. \n",
    "##### Ensure that your code is clear and readable.\n",
    "##### Comments and Markdown notes are advised to direct attention to pieces of code you deem useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "###...code...###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature selection\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "def imbalance(df):\n",
    "    df['f_purchase_lh'] = df['f_purchase_lh'].fillna(-999)\n",
    "    X = df.drop('f_purchase_lh', axis=1)\n",
    "    y = df['f_purchase_lh']\n",
    "    target_column = 'f_purchase_lh'\n",
    "    #handle imbalance\n",
    "    smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
    "    X_resampled, y_resampled = smote.fit_resample(X, y)\n",
    "    df_resampled = pd.concat([pd.DataFrame(X_resampled, columns=X.columns), pd.Series(y_resampled, name=target_column)], axis=1)\n",
    "    return df_resampled\n",
    "\n",
    "def feature_selection(df_resampled):\n",
    "    \n",
    "    X_resampled=df_resampled.drop('f_purchase_lh', axis=1)\n",
    "    \n",
    "    # Variance Threshold\n",
    "    selector = VarianceThreshold(threshold=0.05) \n",
    "    X_resampled_high_variance = selector.fit_transform(X_resampled)\n",
    "    \n",
    "    selected_indices = selector.get_support(indices=True)\n",
    "    selected_df = pd.DataFrame(X_resampled_high_variance, columns=X_resampled.columns[selected_indices])\n",
    "    selected_df['f_purchase_lh'] = df_resampled['f_purchase_lh']\n",
    "    \n",
    "    return selected_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementing Logistic Regression using sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "def scale_and_train(selected_df):\n",
    "    X = selected_df.drop('f_purchase_lh', axis=1)\n",
    "    y = selected_df['f_purchase_lh']\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,y, test_size= 0.2)\n",
    "    modelLogistic = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "    scaler = StandardScaler()\n",
    "    x_train_scaled = scaler.fit_transform(x_train)\n",
    "    x_test_scaled = scaler.transform(x_test)\n",
    "    \n",
    "    modelLogistic.fit(x_train_scaled,y_train)\n",
    "    \n",
    "    # print(\"The intercept b0= \", modelLogistic.intercept_)\n",
    "    \n",
    "    # print(\"The coefficient b1= \", modelLogistic.coef_)\n",
    "    #Make prediction for the test data\n",
    "    y_pred= modelLogistic.predict(x_test_scaled)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy test\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "def test(y_test, y_pred):\n",
    "    ConfusionMatrix = confusion_matrix(y_test, y_pred)\n",
    "    TP= ConfusionMatrix[1,1] #True positive\n",
    "    TN= ConfusionMatrix[0,0] #True negative\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(ConfusionMatrix)\n",
    "    Total=len(y_test)\n",
    "    return accuracy_score(y_test, y_pred)\n",
    "    # return (TN+TP)/Total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The cell below is **NOT** to be removed\n",
    "##### The function is to be amended so that it accepts the given input (dataframe) and returns the required output (list). \n",
    "##### It is recommended to test the function out prior to submission\n",
    "-------------------------------------------------------------------------------------------------------------------------------\n",
    "##### The hidden_data parsed into the function below will have the same layout columns wise as the dataset *SENT* to you\n",
    "##### Thus, ensure that steps taken to modify the initial dataset to fit into the model are also carried out in the function below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from ydata_profiling import ProfileReport\n",
    "def testing_hidden_data(hidden_data: pd.DataFrame) -> list:\n",
    "    '''DO NOT REMOVE THIS FUNCTION.\n",
    "\n",
    "The function accepts a dataframe as input and return an iterable (list)\n",
    "of binary classes as output.\n",
    "\n",
    "The function should be coded to test on hidden data\n",
    "and should include any preprocessing functions needed for your model to perform. \n",
    "    \n",
    "All relevant code MUST be included in this function.'''\n",
    "\n",
    "    df = data_cleaning(hidden_data)\n",
    "    df = encode(df)\n",
    "    df_resampled = imbalance(df)\n",
    "    selected_df = feature_selection(df_resampled)\n",
    " \n",
    "    result = [] \n",
    "    #scale and train data \n",
    "    X = selected_df.drop('f_purchase_lh', axis=1)\n",
    "    y = selected_df['f_purchase_lh']\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X,y, test_size= 0.2)\n",
    "    modelLogistic = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "    scaler = StandardScaler()\n",
    "    x_train_scaled = scaler.fit_transform(x_train)\n",
    "    x_test_scaled = scaler.transform(x_test)\n",
    "    \n",
    "    modelLogistic.fit(x_train_scaled,y_train)\n",
    "   \n",
    "    #Make prediction for the test data\n",
    "    y_pred = modelLogistic.predict(x_test_scaled)\n",
    "    accuracy = test(y_test, y_pred)\n",
    "    print(\"Accuracy from confusion matrix is \", accuracy)\n",
    "    result = y_pred.tolist()\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cell to check testing_hidden_data function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[3174  313]\n",
      " [ 493 2933]]\n",
      "Accuracy from confusion matrix is  0.8834080717488789\n",
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     ]
    }
   ],
   "source": [
    "# This cell should output a list of predictions.\n",
    "test_df = pd.read_parquet(filepath)\n",
    "print(testing_hidden_data(test_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Please have the filename renamed and ensure that it can be run with the requirements above being met. All the best!"
   ]
  },
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