{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe903da7",
   "metadata": {},
   "source": [
    "# Simple lin regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bead5b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ebfa951",
   "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>Customer ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income (USD)</th>\n",
       "      <th>Income Stability</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Type of Employment</th>\n",
       "      <th>Location</th>\n",
       "      <th>Loan Amount Request (USD)</th>\n",
       "      <th>...</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>No. of Defaults</th>\n",
       "      <th>Has Active Credit Card</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Property Age</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Property Location</th>\n",
       "      <th>Co-Applicant</th>\n",
       "      <th>Property Price</th>\n",
       "      <th>Loan Sanction Amount (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5891</th>\n",
       "      <td>C-43900</td>\n",
       "      <td>Barbie Bays</td>\n",
       "      <td>M</td>\n",
       "      <td>47</td>\n",
       "      <td>4251.22</td>\n",
       "      <td>Low</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>49032.72</td>\n",
       "      <td>...</td>\n",
       "      <td>659.70</td>\n",
       "      <td>0</td>\n",
       "      <td>Active</td>\n",
       "      <td>53</td>\n",
       "      <td>4251.22</td>\n",
       "      <td>1</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>85798.87</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16100</th>\n",
       "      <td>C-46168</td>\n",
       "      <td>Luetta Soltis</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>1886.31</td>\n",
       "      <td>Low</td>\n",
       "      <td>Working</td>\n",
       "      <td>Managers</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>46588.26</td>\n",
       "      <td>...</td>\n",
       "      <td>871.61</td>\n",
       "      <td>1</td>\n",
       "      <td>Active</td>\n",
       "      <td>678</td>\n",
       "      <td>1886.31</td>\n",
       "      <td>1</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>0</td>\n",
       "      <td>63030.08</td>\n",
       "      <td>37270.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13554</th>\n",
       "      <td>C-43688</td>\n",
       "      <td>Alpha Olivier</td>\n",
       "      <td>M</td>\n",
       "      <td>44</td>\n",
       "      <td>2244.67</td>\n",
       "      <td>Low</td>\n",
       "      <td>Working</td>\n",
       "      <td>Drivers</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>51026.64</td>\n",
       "      <td>...</td>\n",
       "      <td>598.07</td>\n",
       "      <td>1</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>899</td>\n",
       "      <td>2244.67</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>59155.56</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10644</th>\n",
       "      <td>C-26099</td>\n",
       "      <td>Azzie Pharr</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>3378.07</td>\n",
       "      <td>Low</td>\n",
       "      <td>Working</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semi-Urban</td>\n",
       "      <td>34458.21</td>\n",
       "      <td>...</td>\n",
       "      <td>710.27</td>\n",
       "      <td>1</td>\n",
       "      <td>Unpossessed</td>\n",
       "      <td>435</td>\n",
       "      <td>3378.07</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>59049.07</td>\n",
       "      <td>22397.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14162</th>\n",
       "      <td>C-20036</td>\n",
       "      <td>Milagros Haydon</td>\n",
       "      <td>F</td>\n",
       "      <td>54</td>\n",
       "      <td>789.73</td>\n",
       "      <td>Low</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rural</td>\n",
       "      <td>79190.09</td>\n",
       "      <td>...</td>\n",
       "      <td>837.13</td>\n",
       "      <td>0</td>\n",
       "      <td>Active</td>\n",
       "      <td>666</td>\n",
       "      <td>789.73</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>100597.22</td>\n",
       "      <td>59392.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer ID             Name Gender  Age  Income (USD) Income Stability  \\\n",
       "5891      C-43900      Barbie Bays      M   47       4251.22              Low   \n",
       "16100     C-46168    Luetta Soltis      F   20       1886.31              Low   \n",
       "13554     C-43688    Alpha Olivier      M   44       2244.67              Low   \n",
       "10644     C-26099      Azzie Pharr      M   42       3378.07              Low   \n",
       "14162     C-20036  Milagros Haydon      F   54        789.73              Low   \n",
       "\n",
       "                 Profession Type of Employment    Location  \\\n",
       "5891   Commercial associate           Laborers  Semi-Urban   \n",
       "16100               Working           Managers  Semi-Urban   \n",
       "13554               Working            Drivers  Semi-Urban   \n",
       "10644               Working                NaN  Semi-Urban   \n",
       "14162  Commercial associate                NaN       Rural   \n",
       "\n",
       "       Loan Amount Request (USD)  ...  Credit Score No. of Defaults  \\\n",
       "5891                    49032.72  ...        659.70               0   \n",
       "16100                   46588.26  ...        871.61               1   \n",
       "13554                   51026.64  ...        598.07               1   \n",
       "10644                   34458.21  ...        710.27               1   \n",
       "14162                   79190.09  ...        837.13               0   \n",
       "\n",
       "      Has Active Credit Card  Property ID  Property Age  Property Type  \\\n",
       "5891                  Active           53       4251.22              1   \n",
       "16100                 Active          678       1886.31              1   \n",
       "13554               Inactive          899       2244.67              1   \n",
       "10644            Unpossessed          435       3378.07              2   \n",
       "14162                 Active          666        789.73              4   \n",
       "\n",
       "      Property Location  Co-Applicant  Property Price  \\\n",
       "5891         Semi-Urban             1        85798.87   \n",
       "16100        Semi-Urban             0        63030.08   \n",
       "13554             Urban             1        59155.56   \n",
       "10644             Rural             1        59049.07   \n",
       "14162             Rural             1       100597.22   \n",
       "\n",
       "       Loan Sanction Amount (USD)  \n",
       "5891                         0.00  \n",
       "16100                    37270.61  \n",
       "13554                        0.00  \n",
       "10644                    22397.84  \n",
       "14162                    59392.57  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2056d54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID                       0\n",
       "Name                              0\n",
       "Gender                           53\n",
       "Age                               0\n",
       "Income (USD)                   4576\n",
       "Income Stability               1683\n",
       "Profession                        0\n",
       "Type of Employment             7270\n",
       "Location                          0\n",
       "Loan Amount Request (USD)         0\n",
       "Current Loan Expenses (USD)     172\n",
       "Expense Type 1                    0\n",
       "Expense Type 2                    0\n",
       "Dependents                     2493\n",
       "Credit Score                   1703\n",
       "No. of Defaults                   0\n",
       "Has Active Credit Card         1566\n",
       "Property ID                       0\n",
       "Property Age                   4850\n",
       "Property Type                     0\n",
       "Property Location               356\n",
       "Co-Applicant                      0\n",
       "Property Price                    0\n",
       "Loan Sanction Amount (USD)      340\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocess\n",
    "# figure out where missing\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09b7a7e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan Amount Request (USD)        0\n",
       "Loan Sanction Amount (USD)     340\n",
       "Income (USD)                  4576\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf = df[[\"Loan Amount Request (USD)\", \"Loan Sanction Amount (USD)\", \"Income (USD)\"]].copy()\n",
    "ndf.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "137d6590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan Amount Request (USD)     0\n",
       "Loan Sanction Amount (USD)    0\n",
       "Income (USD)                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf = ndf.dropna()\n",
    "ndf.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "886d23ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.max of 0        1933.05\n",
       "1        4952.91\n",
       "2         988.19\n",
       "4        2614.77\n",
       "5        1234.92\n",
       "          ...   \n",
       "29994    2250.19\n",
       "29995    4969.41\n",
       "29996    1606.88\n",
       "29998    2417.71\n",
       "29999    3068.24\n",
       "Name: Income (USD), Length: 25167, dtype: float64>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf[\"Income (USD)\"].max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b64e2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Income (USD)', 'Loan Amount Request (USD)',\n",
       "       'Current Loan Expenses (USD)', 'Dependents', 'Credit Score',\n",
       "       'No. of Defaults', 'Property ID', 'Property Age', 'Property Type',\n",
       "       'Co-Applicant', 'Property Price', 'Loan Sanction Amount (USD)'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['number']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a25a628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for col in ndf.columns:\n",
    "    plt.title(f\"Distribuition of {col}\", fontweight=\"bold\")\n",
    "    plt.hist(df[col], bins=100)\n",
    "    plt.xlabel(\"Amount\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30830e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Feature vs Target scatter plot\")\n",
    "plt.ylabel(\"Request\")\n",
    "plt.xlabel(\"Sanctioned\")\n",
    "plt.scatter(ndf[\"Loan Amount Request (USD)\"], ndf[\"Loan Sanction Amount (USD)\"], alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4fa6f54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = ndf[[\"Loan Amount Request (USD)\", \"Income (USD)\"]]\n",
    "Y = ndf[\"Loan Sanction Amount (USD)\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=69)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90a99593",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "def eval_model(true, pred):\n",
    "    mae = mean_absolute_error(true, pred)\n",
    "    mse = mean_squared_error(true, pred)\n",
    "    r2 = r2_score(true, pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "\n",
    "    print(f\"MAE: {mae}\")\n",
    "    print(f\"MSE: {mse}\")\n",
    "    print(f\"RMSE: {rmse}\")\n",
    "    print(f\"R2: {r2}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b8a9afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "MAE: 25620.56183093815\n",
      "MSE: 1177872057.004859\n",
      "RMSE: 34320.14069034186\n",
      "R2: -0.02024046147488079\n",
      "\n",
      "Test\n",
      "MAE: 25091.961835252336\n",
      "MSE: 1136719282.8628092\n",
      "RMSE: 33715.267800550086\n",
      "R2: -0.027915297192329103\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y_train_pred = model.predict(x_train)\n",
    "y_test_pred = model.predict(x_test)\n",
    "\n",
    "print(\"Train\")\n",
    "eval_model(y_train_pred, y_train)\n",
    "\n",
    "print(\"Test\")\n",
    "eval_model(y_test_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c403e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "MAE: 25620.56183093819\n",
      "MSE: 1177872057.0048587\n",
      "RMSE: 34320.140690341854\n",
      "R2: -0.0202404614749101\n",
      "\n",
      "Test\n",
      "MAE: 25091.961835251743\n",
      "MSE: 1136719282.862604\n",
      "RMSE: 33715.26780054704\n",
      "R2: -0.02791529719238417\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_model = Ridge()\n",
    "\n",
    "ridge_model.fit(x_train, y_train)\n",
    "\n",
    "y_train_pred = ridge_model.predict(x_train)\n",
    "y_test_pred = ridge_model.predict(x_test)\n",
    "\n",
    "print(\"Train\")\n",
    "eval_model(y_train_pred, y_train)\n",
    "\n",
    "print(\"Test\")\n",
    "eval_model(y_test_pred, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1339eeb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "MAE: 25620.561833937863\n",
      "MSE: 1177872057.0048592\n",
      "RMSE: 34320.14069034186\n",
      "R2: -0.02024046224516929\n",
      "\n",
      "Test\n",
      "MAE: 25091.961711023734\n",
      "MSE: 1136719241.3988392\n",
      "RMSE: 33715.26718563623\n",
      "R2: -0.027915303225953814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso_model = Lasso()\n",
    "\n",
    "lasso_model.fit(x_train, y_train)\n",
    "\n",
    "y_train_pred = lasso_model.predict(x_train)\n",
    "y_test_pred = lasso_model.predict(x_test)\n",
    "\n",
    "print(\"Train\")\n",
    "eval_model(y_train_pred, y_train)\n",
    "\n",
    "print(\"Test\")\n",
    "eval_model(y_test_pred, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.13.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}