Sapnil/Custom_Model_Deployment

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 10,6   "metadata": {},7   "outputs": [8    {9     "name": "stdout",10     "output_type": "stream",11     "text": [12      "Note: you may need to restart the kernel to use updated packages.\n",13      "Note: you may need to restart the kernel to use updated packages.\n"14     ]15    }16   ],17   "source": [18    "%pip install -q gradio\n",19    "%pip install -q catboost"20   ]21  },22  {23   "cell_type": "code",24   "execution_count": 11,25   "metadata": {},26   "outputs": [],27   "source": [28    "import pandas as pd\n",29    "import numpy as np\n",30    "import seaborn as sns"31   ]32  },33  {34   "cell_type": "code",35   "execution_count": 12,36   "metadata": {},37   "outputs": [],38   "source": [39    "sns.set_theme()"40   ]41  },42  {43   "cell_type": "code",44   "execution_count": 13,45   "metadata": {},46   "outputs": [],47   "source": [48    "df = sns.load_dataset(\"titanic\")"49   ]50  },51  {52   "cell_type": "code",53   "execution_count": 14,54   "metadata": {},55   "outputs": [56    {57     "data": {58      "text/plain": [59       "survived          int64\n",60       "pclass            int64\n",61       "sex              object\n",62       "age             float64\n",63       "sibsp             int64\n",64       "parch             int64\n",65       "fare            float64\n",66       "embarked         object\n",67       "class          category\n",68       "who              object\n",69       "adult_male         bool\n",70       "deck           category\n",71       "embark_town      object\n",72       "alive            object\n",73       "alone              bool\n",74       "dtype: object"75      ]76     },77     "execution_count": 14,78     "metadata": {},79     "output_type": "execute_result"80    }81   ],82   "source": [83    "df.dtypes"84   ]85  },86  {87   "cell_type": "code",88   "execution_count": 15,89   "metadata": {},90   "outputs": [91    {92     "data": {93      "text/html": [94       "<div>\n",95       "<style scoped>\n",96       "    .dataframe tbody tr th:only-of-type {\n",97       "        vertical-align: middle;\n",98       "    }\n",99       "\n",100       "    .dataframe tbody tr th {\n",101       "        vertical-align: top;\n",102       "    }\n",103       "\n",104       "    .dataframe thead th {\n",105       "        text-align: right;\n",106       "    }\n",107       "</style>\n",108       "<table border=\"1\" class=\"dataframe\">\n",109       "  <thead>\n",110       "    <tr style=\"text-align: right;\">\n",111       "      <th></th>\n",112       "      <th>survived</th>\n",113       "      <th>pclass</th>\n",114       "      <th>sex</th>\n",115       "      <th>age</th>\n",116       "      <th>sibsp</th>\n",117       "      <th>parch</th>\n",118       "      <th>fare</th>\n",119       "      <th>embarked</th>\n",120       "      <th>class</th>\n",121       "      <th>who</th>\n",122       "      <th>adult_male</th>\n",123       "      <th>deck</th>\n",124       "      <th>embark_town</th>\n",125       "      <th>alive</th>\n",126       "      <th>alone</th>\n",127       "    </tr>\n",128       "  </thead>\n",129       "  <tbody>\n",130       "    <tr>\n",131       "      <th>0</th>\n",132       "      <td>0</td>\n",133       "      <td>3</td>\n",134       "      <td>male</td>\n",135       "      <td>22.0</td>\n",136       "      <td>1</td>\n",137       "      <td>0</td>\n",138       "      <td>7.2500</td>\n",139       "      <td>S</td>\n",140       "      <td>Third</td>\n",141       "      <td>man</td>\n",142       "      <td>True</td>\n",143       "      <td>NaN</td>\n",144       "      <td>Southampton</td>\n",145       "      <td>no</td>\n",146       "      <td>False</td>\n",147       "    </tr>\n",148       "    <tr>\n",149       "      <th>1</th>\n",150       "      <td>1</td>\n",151       "      <td>1</td>\n",152       "      <td>female</td>\n",153       "      <td>38.0</td>\n",154       "      <td>1</td>\n",155       "      <td>0</td>\n",156       "      <td>71.2833</td>\n",157       "      <td>C</td>\n",158       "      <td>First</td>\n",159       "      <td>woman</td>\n",160       "      <td>False</td>\n",161       "      <td>C</td>\n",162       "      <td>Cherbourg</td>\n",163       "      <td>yes</td>\n",164       "      <td>False</td>\n",165       "    </tr>\n",166       "  </tbody>\n",167       "</table>\n",168       "</div>"169      ],170      "text/plain": [171       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",172       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",173       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",174       "\n",175       "     who  adult_male deck  embark_town alive  alone  \n",176       "0    man        True  NaN  Southampton    no  False  \n",177       "1  woman       False    C    Cherbourg   yes  False  "178      ]179     },180     "execution_count": 15,181     "metadata": {},182     "output_type": "execute_result"183    }184   ],185   "source": [186    "df.head(2)"187   ]188  },189  {190   "cell_type": "code",191   "execution_count": 16,192   "metadata": {},193   "outputs": [194    {195     "name": "stderr",196     "output_type": "stream",197     "text": [198      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",199      "  if pd.api.types.is_categorical_dtype(vector):\n",200      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",201      "  if pd.api.types.is_categorical_dtype(vector):\n",202      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",203      "  if pd.api.types.is_categorical_dtype(vector):\n"204     ]205    },206    {207     "data": {208      "text/plain": [209       "<Axes: xlabel='pclass', ylabel='count'>"210      ]211     },212     "execution_count": 16,213     "metadata": {},214     "output_type": "execute_result"215    },216    {217     "data": {218      "image/png": 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",219      "text/plain": [220       "<Figure size 640x480 with 1 Axes>"221      ]222     },223     "metadata": {},224     "output_type": "display_data"225    }226   ],227   "source": [228    "sns.countplot(x = \"pclass\", data = df)"229   ]230  },231  {232   "cell_type": "code",233   "execution_count": 17,234   "metadata": {},235   "outputs": [236    {237     "name": "stderr",238     "output_type": "stream",239     "text": [240      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",241      "  if pd.api.types.is_categorical_dtype(vector):\n",242      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",243      "  if pd.api.types.is_categorical_dtype(vector):\n",244      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",245      "  if pd.api.types.is_categorical_dtype(vector):\n"246     ]247    },248    {249     "data": {250      "text/plain": [251       "<Axes: xlabel='who', ylabel='count'>"252      ]253     },254     "execution_count": 17,255     "metadata": {},256     "output_type": "execute_result"257    },258    {259     "data": {260      "image/png": 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",261      "text/plain": [262       "<Figure size 640x480 with 1 Axes>"263      ]264     },265     "metadata": {},266     "output_type": "display_data"267    }268   ],269   "source": [270    "sns.countplot(x = \"who\", data = df)"271   ]272  },273  {274   "cell_type": "code",275   "execution_count": 18,276   "metadata": {},277   "outputs": [278    {279     "name": "stderr",280     "output_type": "stream",281     "text": [282      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",283      "  if pd.api.types.is_categorical_dtype(vector):\n",284      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",285      "  if pd.api.types.is_categorical_dtype(vector):\n",286      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",287      "  if pd.api.types.is_categorical_dtype(vector):\n"288     ]289    },290    {291     "data": {292      "text/plain": [293       "<Axes: xlabel='embarked', ylabel='count'>"294      ]295     },296     "execution_count": 18,297     "metadata": {},298     "output_type": "execute_result"299    },300    {301     "data": {302      "image/png": 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",303      "text/plain": [304       "<Figure size 640x480 with 1 Axes>"305      ]306     },307     "metadata": {},308     "output_type": "display_data"309    }310   ],311   "source": [312    "sns.countplot(x = \"embarked\", data = df)"313   ]314  },315  {316   "cell_type": "code",317   "execution_count": 19,318   "metadata": {},319   "outputs": [320    {321     "data": {322      "text/plain": [323       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",324       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",325       "       'alive', 'alone'],\n",326       "      dtype='object')"327      ]328     },329     "execution_count": 19,330     "metadata": {},331     "output_type": "execute_result"332    }333   ],334   "source": [335    "df.columns"336   ]337  },338  {339   "cell_type": "code",340   "execution_count": 20,341   "metadata": {},342   "outputs": [],343   "source": [344    "# Identifying features, we are keeping very few features as we want to simulate this using gradio\n",345    "features = [ 'pclass', 'sex', 'age', 'fare',\n",346    "       'embarked']\n",347    "target = 'survived'"348   ]349  },350  {351   "cell_type": "code",352   "execution_count": 21,353   "metadata": {},354   "outputs": [355    {356     "data": {357      "text/plain": [358       "pclass      0.000000\n",359       "sex         0.000000\n",360       "age         0.198653\n",361       "fare        0.000000\n",362       "embarked    0.002245\n",363       "dtype: float64"364      ]365     },366     "execution_count": 21,367     "metadata": {},368     "output_type": "execute_result"369    }370   ],371   "source": [372    "df[features].isna().sum()/df.shape[0]"373   ]374  },375  {376   "cell_type": "code",377   "execution_count": 22,378   "metadata": {},379   "outputs": [380    {381     "data": {382      "text/plain": [383       "embarked\n",384       "S    644\n",385       "C    168\n",386       "Q     77\n",387       "Name: count, dtype: int64"388      ]389     },390     "execution_count": 22,391     "metadata": {},392     "output_type": "execute_result"393    }394   ],395   "source": [396    "df['embarked'].value_counts()"397   ]398  },399  {400   "cell_type": "code",401   "execution_count": 23,402   "metadata": {},403   "outputs": [],404   "source": [405    "# Filling missing values\n",406    "df['age'].fillna(np.nanquantile(df['age'], 0.5), inplace = True)\n",407    "df['embarked'].fillna(\"S\", inplace = True)"408   ]409  },410  {411   "cell_type": "code",412   "execution_count": 24,413   "metadata": {},414   "outputs": [],415   "source": [416    "from catboost import CatBoostClassifier"417   ]418  },419  {420   "cell_type": "code",421   "execution_count": 25,422   "metadata": {},423   "outputs": [],424   "source": [425    "clf = CatBoostClassifier()"426   ]427  },428  {429   "cell_type": "code",430   "execution_count": 26,431   "metadata": {},432   "outputs": [],433   "source": [434    "# Creating features and target\n",435    "X = df[features]\n",436    "y = df[target]"437   ]438  },439  {440   "cell_type": "code",441   "execution_count": 27,442   "metadata": {},443   "outputs": [444    {445     "name": "stdout",446     "output_type": "stream",447     "text": [448      "Learning rate set to 0.009807\n",449      "0:\tlearn: 0.6861079\ttotal: 51.3ms\tremaining: 51.3s\n",450      "1:\tlearn: 0.6807116\ttotal: 52.8ms\tremaining: 26.3s\n",451      "2:\tlearn: 0.6752663\ttotal: 54.9ms\tremaining: 18.3s\n",452      "3:\tlearn: 0.6712222\ttotal: 56ms\tremaining: 14s\n",453      "4:\tlearn: 0.6661659\ttotal: 57.4ms\tremaining: 11.4s\n",454      "5:\tlearn: 0.6609001\ttotal: 58.9ms\tremaining: 9.76s\n",455      "6:\tlearn: 0.6547619\ttotal: 61.6ms\tremaining: 8.73s\n",456      "7:\tlearn: 0.6488790\ttotal: 63.9ms\tremaining: 7.92s\n",457      "8:\tlearn: 0.6436617\ttotal: 66.2ms\tremaining: 7.29s\n",458      "9:\tlearn: 0.6391954\ttotal: 67.9ms\tremaining: 6.72s\n",459      "10:\tlearn: 0.6348349\ttotal: 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