main_model.ipynb

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  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split   \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30                 1          0.01605   \n",
       "1           DRC01         5.92                 2          0.01928   \n",
       "2           FDN15        17.50                 1          0.01676   \n",
       "3           FDX07        19.20                 2          0.00000   \n",
       "4           NCD19         8.93                 1          0.00000   \n",
       "\n",
       "               Item_Type  Item_MRP  Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092                  1   \n",
       "1            Soft Drinks   48.2692                  2   \n",
       "2                   Meat  141.6180                  1   \n",
       "3  Fruits and Vegetables  182.0950                  3   \n",
       "4              Household   53.8614                  4   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  Units_Sold  Item_Visibility_Adjusted  \n",
       "0  Supermarket Type1             3735.0        15.0                  1.604730  \n",
       "1  Supermarket Type2              443.0         9.0                  1.927822  \n",
       "2  Supermarket Type1             2097.0        15.0                  1.676007  \n",
       "3      Grocery Store              732.0         4.0                  0.000000  \n",
       "4  Supermarket Type1              995.0        18.0                  0.000000  "
      ],
      "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>Item_Identifier</th>\n      <th>Item_Weight</th>\n      <th>Item_Fat_Content</th>\n      <th>Item_Visibility</th>\n      <th>Item_Type</th>\n      <th>Item_MRP</th>\n      <th>Outlet_Identifier</th>\n      <th>Outlet_Establishment_Year</th>\n      <th>Outlet_Size</th>\n      <th>Outlet_Location_Type</th>\n      <th>Outlet_Type</th>\n      <th>Item_Outlet_Sales</th>\n      <th>Units_Sold</th>\n      <th>Item_Visibility_Adjusted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>FDA15</td>\n      <td>9.30</td>\n      <td>1</td>\n      <td>0.01605</td>\n      <td>Dairy</td>\n      <td>249.8092</td>\n      <td>1</td>\n      <td>1999</td>\n      <td>Medium</td>\n      <td>Tier 1</td>\n      <td>Supermarket Type1</td>\n      <td>3735.0</td>\n      <td>15.0</td>\n      <td>1.604730</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DRC01</td>\n      <td>5.92</td>\n      <td>2</td>\n      <td>0.01928</td>\n      <td>Soft Drinks</td>\n      <td>48.2692</td>\n      <td>2</td>\n      <td>2009</td>\n      <td>Medium</td>\n      <td>Tier 3</td>\n      <td>Supermarket Type2</td>\n      <td>443.0</td>\n      <td>9.0</td>\n      <td>1.927822</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>FDN15</td>\n      <td>17.50</td>\n      <td>1</td>\n      <td>0.01676</td>\n      <td>Meat</td>\n      <td>141.6180</td>\n      <td>1</td>\n      <td>1999</td>\n      <td>Medium</td>\n      <td>Tier 1</td>\n      <td>Supermarket Type1</td>\n      <td>2097.0</td>\n      <td>15.0</td>\n      <td>1.676007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>FDX07</td>\n      <td>19.20</td>\n      <td>2</td>\n      <td>0.00000</td>\n      <td>Fruits and Vegetables</td>\n      <td>182.0950</td>\n      <td>3</td>\n      <td>1998</td>\n      <td>Small</td>\n      <td>Tier 3</td>\n      <td>Grocery Store</td>\n      <td>732.0</td>\n      <td>4.0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NCD19</td>\n      <td>8.93</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>Household</td>\n      <td>53.8614</td>\n      <td>4</td>\n      <td>1987</td>\n      <td>High</td>\n      <td>Tier 3</td>\n      <td>Supermarket Type1</td>\n      <td>995.0</td>\n      <td>18.0</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "df = pd.read_csv('data/sales_predictions_clean.csv')\n",
    "df['Item_Outlet_Sales'] = df['Item_Outlet_Sales'].round()\n",
    "df['Units_Sold'] = (df['Item_Outlet_Sales'] / df['Item_MRP']).round()\n",
    "df['Item_Visibility_Adjusted'] = df['Item_Visibility']*100\n",
    "df['Item_Visibility'] = df['Item_Visibility'].round(5)\n",
    "# df.head()\n",
    "Outlets = df['Outlet_Identifier'].unique().tolist()\n",
    "Item_Types = df['Item_Type'].unique().tolist()\n",
    "Fat_Contents = df['Item_Fat_Content'].unique().tolist()\n",
    "\n",
    "Model_Df = df\n",
    "for i in range(len(Fat_Contents)):\n",
    "    Model_Df.replace(Fat_Contents[i], value=i+1, inplace=True)\n",
    "for i in range(len(Outlets)):\n",
    "    Model_Df.replace(Outlets[i], value=i+1, inplace=True)\n",
    "# for i in range(len(Item_Types)):\n",
    "#     Model_Df.replace(Item_Types[i], value=i+1, inplace=True)\n",
    "Model_Df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Dairy': 0.3313,\n",
       " 'Soft Drinks': 0.3251,\n",
       " 'Meat': 0.3839,\n",
       " 'Fruits and Vegetables': 0.368,\n",
       " 'Household': 0.276,\n",
       " 'Baking Goods': 0.2735,\n",
       " 'Snack Foods': 0.2634,\n",
       " 'Frozen Foods': 0.3243,\n",
       " 'Breakfast': 0.2989,\n",
       " 'Health and Hygiene': 0.3549,\n",
       " 'Hard Drinks': 0.4954,\n",
       " 'Canned': 0.4234,\n",
       " 'Breads': 0.357,\n",
       " 'Starchy Foods': 0.3613,\n",
       " 'Others': 0.2026,\n",
       " 'Seafood': -0.0146}"
      ]
     },
     "metadata": {},
     "execution_count": 122
    }
   ],
   "source": [
    "lr_scores = {}\n",
    "for i in range(len(Item_Types)):\n",
    "    X = Model_Df[Model_Df['Item_Type']==Item_Types[i]].drop(columns=[\n",
    "        'Item_Identifier',\n",
    "        'Item_Type',\n",
    "        # 'Item_Weight',\n",
    "        # 'Item_Fat_Content',\n",
    "        # 'Item_MRP',\n",
    "        'Item_Visibility', ### THIS TANKS THE SCORE\n",
    "        'Outlet_Identifier',\n",
    "        'Outlet_Establishment_Year', \n",
    "        'Outlet_Size',\n",
    "        'Outlet_Type',\n",
    "        'Outlet_Location_Type',\n",
    "        'Item_Outlet_Sales',\n",
    "        'Units_Sold',\n",
    "        'Item_Visibility_Adjusted'\n",
    "        ])\n",
    "    y = Model_Df[Model_Df['Item_Type']==Item_Types[i]]['Item_Outlet_Sales'].values\n",
    "    # print(f'y = {y.shape}, X= {X.shape}')\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 31)\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "\n",
    "    reg = LinearRegression(fit_intercept=True)\n",
    "    reg.fit(X_train,y_train)\n",
    "    reg.predict(X_test)\n",
    "\n",
    "    score = reg.score(X_test, y_test)\n",
    "    # print(score)\n",
    "    lr_scores[Item_Types[i]] = score.round(4)\n",
    "\n",
    "lr_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Dairy': 0.0292,\n",
       " 'Soft Drinks': 0.0536,\n",
       " 'Meat': 0.0187,\n",
       " 'Fruits and Vegetables': 0.0227,\n",
       " 'Household': 0.0351,\n",
       " 'Baking Goods': 0.0309,\n",
       " 'Snack Foods': 0.0133,\n",
       " 'Frozen Foods': 0.014,\n",
       " 'Breakfast': 0.0,\n",
       " 'Health and Hygiene': 0.0308,\n",
       " 'Hard Drinks': 0.0,\n",
       " 'Canned': 0.0307,\n",
       " 'Breads': 0.0317,\n",
       " 'Starchy Foods': 0.027,\n",
       " 'Others': 0.0,\n",
       " 'Seafood': 0.0625}"
      ]
     },
     "metadata": {},
     "execution_count": 124
    }
   ],
   "source": [
    "knn_scores = {}\n",
    "for i in range(len(Item_Types)):\n",
    "    X = Model_Df[Model_Df['Item_Type']==Item_Types[i]].drop(columns=[\n",
    "        'Item_Identifier',\n",
    "        'Item_Type',\n",
    "        # 'Item_Weight',\n",
    "        # 'Item_Fat_Content',\n",
    "        # 'Item_MRP',\n",
    "        'Item_Visibility', ### THIS TANKS THE SCORE\n",
    "        'Outlet_Identifier',\n",
    "        'Outlet_Establishment_Year', \n",
    "        'Outlet_Size',\n",
    "        'Outlet_Type',\n",
    "        'Outlet_Location_Type',\n",
    "        'Item_Outlet_Sales',\n",
    "        'Units_Sold',\n",
    "        'Item_Visibility_Adjusted'\n",
    "        ])\n",
    "    y = Model_Df[Model_Df['Item_Type']==Item_Types[i]]['Item_Outlet_Sales'].values\n",
    "    # print(f'y = {y.shape}, X= {X.shape}')\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 31)\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    knn = KNeighborsClassifier(n_neighbors= 2)\n",
    "    # print(knn)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_train)\n",
    "    predictions\n",
    "    score = knn.score(X_test, y_test)\n",
    "    knn_scores[Item_Types[i]] = score.round(4)\n",
    "\n",
    "knn_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "score = {}\n",
    "# range(1, (len(X_train)//10))\n",
    "for i in range(1, len(X_train), 100):\n",
    "    # print(i)\n",
    "    knn = KNeighborsClassifier(n_neighbors= i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_train)\n",
    "    test_predictions = knn.predict(X_test)\n",
    "    score[i] = knn.score(X_test, y_test)\n",
    "\n",
    "plt.plot(score.values());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "score = {}\n",
    "# range(1, (len(X_train)//10))\n",
    "for i in range(1, 1000, 10):\n",
    "    # print(i)\n",
    "    knn = KNeighborsClassifier(n_neighbors= i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_train)\n",
    "    test_predictions = knn.predict(X_test)\n",
    "    score[i] = knn.score(X_test, y_test)\n",
    "\n",
    "plt.plot(score.values());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.331303153521491\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 31)\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "reg = LinearRegression(fit_intercept=True)\n",
    "reg.fit(X_train,y_train)\n",
    "reg.predict(X_test)\n",
    "\n",
    "score = reg.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}