01 - Loan Status using Logistic Regression.ipynb

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   "source": [
    "# ------------------------------------------------------------------\n",
    "# Build the Logistic Regression Model\n",
    "# Predict the loan approval status based on \n",
    "# Gender, Marital Status, Credit History, Income and Loan Amount\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "# Import Libraries\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data and Create a copy\n",
    "LoanData = pd.read_csv(\"01Exercise1.csv\")\n",
    "LoanPrep = LoanData.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender     13\n",
       "married     3\n",
       "ch         50\n",
       "income      0\n",
       "loanamt    22\n",
       "status      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out columns with missing values\n",
    "LoanPrep.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace Missing Values. Drop the rows.\n",
    "LoanPrep = LoanPrep.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop irrelevant columns based on business sense\n",
    "LoanPrep = LoanPrep.drop(['gender'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Dummy variables\n",
    "LoanPrep.dtypes\n",
    "LoanPrep = pd.get_dummies(LoanPrep, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data (Income and Loan Amount) Using StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar_ = StandardScaler()\n",
    "\n",
    "LoanPrep['income'] = scalar_.fit_transform(LoanPrep[['income']])\n",
    "LoanPrep['loanamt'] = scalar_.fit_transform(LoanPrep[['loanamt']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the X (Independent) and Y (Dependent) dataframes\n",
    "# -------------------------------------------------------\n",
    "Y = LoanPrep[['status_Y']]\n",
    "X = LoanPrep.drop(['status_Y'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the X and Y dataset into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = \\\n",
    "train_test_split(X, Y, test_size = 0.3, random_state = 1234, stratify=Y)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\Ashish\\Anaconda3\\lib\\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",
      "C:\\Users\\Ashish\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: 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"
     ]
    },
    {
     "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the Logistic Regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the outcome using Test data\n",
    "Y_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the conufsion matrix and get the accuracy/score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test, Y_predict)\n",
    "\n",
    "score = lr.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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