Model.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Importing Necessary Modules</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import log_loss, classification_report, accuracy_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_reader(data):\n",
    "    return pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = file_reader('new_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking out the Categorical Column and Numerical in the Dataset\n",
    "def cat_num(data):\n",
    "    cat_col = []\n",
    "    num_col = []\n",
    "    for feat in data.columns:\n",
    "        if data[feat].dtypes =='O':\n",
    "            cat_col.append(feat)\n",
    "        else:\n",
    "            num_col.append(feat)\n",
    "    return cat_col, num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col, num_col = cat_num(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = df[400000:]\n",
    "\n",
    "train = df[:400000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the dataset for our ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train[num_col].drop(['CHURN'], axis =1)\n",
    "y = train.CHURN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, test_size = 0.25, random_state = 43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb=LGBMClassifier(learning_rate = 0.05,n_estimators=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lgb.predict_proba(X_test)[:,1]\n",
    "pred_x = lgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_test,pred_x,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_test, pred)  #Logloss of 0.2519 on Public Lb and 0.24742\n",
    "\n",
    "#This was my Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = plt.figure(figsize = (10,8))\n",
    "fi = pd.Series(index = X.columns, data = lgm.feature_importances_)\n",
    "_ = fi.sort_values()[-50:].plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = file_reader('sample_submission.csv')\n",
    "sample = sub.copy()\n",
    "sample.CHURN = lgb_test[:,1]\n",
    "sample.to_csv('Sub.csv', index = False)"
   ]
  }
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