analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OneHotEncoder,LabelEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.utils import shuffle\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from keras.models import Sequential\n",
    "from sklearn.preprocessing import scale\n",
    "import tensorflow as tf\n",
    "from keras.layers import Dense,GRU,Embedding\n",
    "from keras.optimizers import Adam\n",
    "import pickle\n",
    "from sklearn.metrics import accuracy_score ,confusion_matrix,r2_score ,mean_absolute_percentage_error,mean_absolute_error,mean_squared_error\n",
    "import random\n",
    "import keras_tuner as kt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"crop_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STATE', 'SOIL_TYPE', 'N_SOIL', 'P_SOIL', 'K_SOIL', 'TEMPERATURE',\n",
       "       'HUMIDITY', 'ph', 'RAINFALL', 'CROP_PRICE', 'CROP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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>STATE</th>\n",
       "      <th>SOIL_TYPE</th>\n",
       "      <th>N_SOIL</th>\n",
       "      <th>P_SOIL</th>\n",
       "      <th>K_SOIL</th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>HUMIDITY</th>\n",
       "      <th>ph</th>\n",
       "      <th>RAINFALL</th>\n",
       "      <th>CROP_PRICE</th>\n",
       "      <th>CROP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>7000</td>\n",
       "      <td>Amaranthus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>5000</td>\n",
       "      <td>Green Banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>7000</td>\n",
       "      <td>Ladies Finger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>7000</td>\n",
       "      <td>Bitter Gourd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>120000</td>\n",
       "      <td>Black pepper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 STATE   SOIL_TYPE  N_SOIL  P_SOIL  K_SOIL  TEMPERATURE  \\\n",
       "0  Andaman and Nicobar  Sandy soil      90      42      43    20.879744   \n",
       "1  Andaman and Nicobar  Sandy soil      85      58      41    21.770462   \n",
       "2  Andaman and Nicobar  Sandy soil      60      55      44    23.004459   \n",
       "3  Andaman and Nicobar  Sandy soil      74      35      40    26.491096   \n",
       "4  Andaman and Nicobar  Sandy soil      78      42      42    20.130175   \n",
       "\n",
       "    HUMIDITY        ph    RAINFALL  CROP_PRICE           CROP  \n",
       "0  82.002744  6.502985  202.935536        7000     Amaranthus  \n",
       "1  80.319644  7.038096  226.655537        5000   Green Banana  \n",
       "2  82.320763  7.840207  263.964248        7000  Ladies Finger  \n",
       "3  80.158363  6.980401  242.864034        7000   Bitter Gourd  \n",
       "4  81.604873  7.628473  262.717340      120000   Black pepper  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE            26\n",
       "SOIL_TYPE        15\n",
       "N_SOIL          137\n",
       "P_SOIL          117\n",
       "K_SOIL           73\n",
       "TEMPERATURE    2200\n",
       "HUMIDITY       2200\n",
       "ph             2200\n",
       "RAINFALL       2200\n",
       "CROP_PRICE      502\n",
       "CROP            149\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['STATE'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amaranthus', 'Green Banana', 'Ladies Finger', 'Bitter Gourd',\n",
       "       'Black pepper', 'Bottle Gourd', 'Brinjal', 'Cabbage', 'Carrot',\n",
       "       'Cauliflower', 'Cluster beans', 'Coconut', 'Colacasia', 'Onion',\n",
       "       'Potato', 'Tomato', 'Bengal Gram', 'Jowar', 'Paddy', 'Lentil',\n",
       "       'Rice', 'Cucumber', 'Field Pea', 'French Beans', 'Green Chilli',\n",
       "       'Knool Khol', 'Pumpkin', 'Raddish', 'Black Gram', 'Green Gram',\n",
       "       'Jute', 'Maida Atta', 'Mustard', 'Wheat Atta', 'Garlic',\n",
       "       'Masur Dal', 'Ridge Gourd', 'Arecanut', 'Red Gram', 'Maize',\n",
       "       'Dry Chillies', 'Groundnut', 'Capsicum', 'Guar', 'Lemon', 'Bajra',\n",
       "       'Castor Seed', 'Coriander', 'Cowpea', 'Drumstick', 'Elephat Yam',\n",
       "       'Ginger', 'Seam', 'Methi Leaves', 'Green Onion', 'Peas cod',\n",
       "       'Pigeon Pea', 'Sponge Gourd', 'Surat Beans', 'Sweet Potato',\n",
       "       'Tinda', 'Cluster Beans', 'Cotton', 'Wheat', 'Chholia',\n",
       "       'Little Gourd', 'Round gourd', 'Leafy Vegetable', 'Mint', 'Papaya',\n",
       "       'Spinach', 'Parval', 'Banana', 'Zizyphus', 'Grapes', 'Kinnow',\n",
       "       'Peas', 'Apple', 'Orange', 'Pomegranate', 'Sapota', 'Mushrooms',\n",
       "       'Sweet Lime', 'Pineapple', 'Guava', 'Turnip', 'Squash', 'Beans',\n",
       "       'Beetroot', 'Chilly Capsicum', 'Green Avare', 'Seemebadnekai',\n",
       "       'Snakeguard', 'Suvarna Gadde', 'Water Melon', 'Copra',\n",
       "       'Amphophalus', 'Ash Gourd', 'Coconut Oil', 'Rubber', 'Cashewnuts',\n",
       "       'Pepper garbled', 'Coconut Seed', 'Long Melon', 'Tapioca',\n",
       "       'Turmeric', 'Mango', 'Amla', 'Duster Beans', 'Soyabean', 'Linseed',\n",
       "       'Niger Seed', 'Moong Dal', 'Lime', 'Musk Melon', 'Pear', 'Rajgir',\n",
       "       'Sweet Pumpkin', 'Tender Coconut', 'Chana Dal', 'Betal Leaves',\n",
       "       'Broken Rice', 'Jaggery', 'Sugar', 'Sesamum', 'Moath Dal',\n",
       "       'Corriander seed', 'Ground Nut Seed', 'Taramira', 'Tobacco',\n",
       "       'Tamarind Fruit', 'Horse Gram', 'Ragi', 'Cumbu', 'Gingelly Oil',\n",
       "       'Varagu', 'Hybrid Cumbu', 'Karamani', 'Thinai', 'Wood', 'Barley',\n",
       "       'Fish', 'Green Peas', 'Tur Dal', 'Urd Dal', 'Mustard Oil', 'Ghee',\n",
       "       'White Pumpkin', 'Plum'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CROP'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SOIL_TYPE</th>\n",
       "      <th>N_SOIL</th>\n",
       "      <th>P_SOIL</th>\n",
       "      <th>K_SOIL</th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>HUMIDITY</th>\n",
       "      <th>ph</th>\n",
       "      <th>RAINFALL</th>\n",
       "      <th>CROP_PRICE</th>\n",
       "      <th>CROP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>7000</td>\n",
       "      <td>Amaranthus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>5000</td>\n",
       "      <td>Green Banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>7000</td>\n",
       "      <td>Ladies Finger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>7000</td>\n",
       "      <td>Bitter Gourd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>120000</td>\n",
       "      <td>Black pepper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SOIL_TYPE  N_SOIL  P_SOIL  K_SOIL  TEMPERATURE   HUMIDITY        ph  \\\n",
       "0  Sandy soil      90      42      43    20.879744  82.002744  6.502985   \n",
       "1  Sandy soil      85      58      41    21.770462  80.319644  7.038096   \n",
       "2  Sandy soil      60      55      44    23.004459  82.320763  7.840207   \n",
       "3  Sandy soil      74      35      40    26.491096  80.158363  6.980401   \n",
       "4  Sandy soil      78      42      42    20.130175  81.604873  7.628473   \n",
       "\n",
       "     RAINFALL  CROP_PRICE           CROP  \n",
       "0  202.935536        7000     Amaranthus  \n",
       "1  226.655537        5000   Green Banana  \n",
       "2  263.964248        7000  Ladies Finger  \n",
       "3  242.864034        7000   Bitter Gourd  \n",
       "4  262.717340      120000   Black pepper  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Carron  Muleya\\AppData\\Local\\Temp\\ipykernel_14128\\3870565311.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_encoded['SOIL_TYPE']=df['SOIL_TYPE'].astype(\"category\").cat.codes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "352/352 [==============================] - 5s 8ms/step - loss: 4.4137 - accuracy: 0.0449\n",
      "Epoch 2/50\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 4.2209 - accuracy: 0.0551\n",
      "Epoch 3/50\n",
      "352/352 [==============================] - 3s 7ms/step - loss: 4.1152 - accuracy: 0.0750\n",
      "Epoch 4/50\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 4.0065 - accuracy: 0.0727\n",
      "Epoch 5/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.9438 - accuracy: 0.0778\n",
      "Epoch 6/50\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 3.9016 - accuracy: 0.0812\n",
      "Epoch 7/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.8603 - accuracy: 0.0784\n",
      "Epoch 8/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.8393 - accuracy: 0.0761\n",
      "Epoch 9/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.8215 - accuracy: 0.0807\n",
      "Epoch 10/50\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 3.8002 - accuracy: 0.0784\n",
      "Epoch 11/50\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 3.7858 - accuracy: 0.0830\n",
      "Epoch 12/50\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 3.7685 - accuracy: 0.0773\n",
      "Epoch 13/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.7514 - accuracy: 0.0750\n",
      "Epoch 14/50\n",
      "352/352 [==============================] - 3s 10ms/step - loss: 3.7397 - accuracy: 0.0818\n",
      "Epoch 15/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.7156 - accuracy: 0.0818\n",
      "Epoch 16/50\n",
      "352/352 [==============================] - 2s 5ms/step - loss: 3.6966 - accuracy: 0.0784\n",
      "Epoch 17/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.6862 - accuracy: 0.0795\n",
      "Epoch 18/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.6683 - accuracy: 0.0801\n",
      "Epoch 19/50\n",
      "352/352 [==============================] - 5s 14ms/step - loss: 3.6593 - accuracy: 0.0835\n",
      "Epoch 20/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.6328 - accuracy: 0.0847\n",
      "Epoch 21/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.6158 - accuracy: 0.0858\n",
      "Epoch 22/50\n",
      "352/352 [==============================] - 1s 4ms/step - loss: 3.6059 - accuracy: 0.0858\n",
      "Epoch 23/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.5955 - accuracy: 0.0875\n",
      "Epoch 24/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.5718 - accuracy: 0.0864\n",
      "Epoch 25/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.5588 - accuracy: 0.0835\n",
      "Epoch 26/50\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 3.5287 - accuracy: 0.0909\n",
      "Epoch 27/50\n",
      "352/352 [==============================] - 5s 13ms/step - loss: 3.5374 - accuracy: 0.0886\n",
      "Epoch 28/50\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 3.5062 - accuracy: 0.0938\n",
      "Epoch 29/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.4972 - accuracy: 0.0938\n",
      "Epoch 30/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.4740 - accuracy: 0.1011\n",
      "Epoch 31/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.4687 - accuracy: 0.0960\n",
      "Epoch 32/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.4499 - accuracy: 0.0943\n",
      "Epoch 33/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.4358 - accuracy: 0.0977\n",
      "Epoch 34/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.4354 - accuracy: 0.1017\n",
      "Epoch 35/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.4077 - accuracy: 0.1074\n",
      "Epoch 36/50\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 3.4009 - accuracy: 0.0994\n",
      "Epoch 37/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.3713 - accuracy: 0.1074\n",
      "Epoch 38/50\n",
      "352/352 [==============================] - 4s 10ms/step - loss: 3.3788 - accuracy: 0.1040\n",
      "Epoch 39/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.3636 - accuracy: 0.1057\n",
      "Epoch 40/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.3560 - accuracy: 0.1097\n",
      "Epoch 41/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.3457 - accuracy: 0.1153\n",
      "Epoch 42/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.3435 - accuracy: 0.1085\n",
      "Epoch 43/50\n",
      "352/352 [==============================] - 2s 7ms/step - loss: 3.3128 - accuracy: 0.1142\n",
      "Epoch 44/50\n",
      "352/352 [==============================] - 4s 11ms/step - loss: 3.3001 - accuracy: 0.1063\n",
      "Epoch 45/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.2953 - accuracy: 0.1063\n",
      "Epoch 46/50\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 3.2759 - accuracy: 0.1114\n",
      "Epoch 47/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.2760 - accuracy: 0.1108\n",
      "Epoch 48/50\n",
      "352/352 [==============================] - 2s 6ms/step - loss: 3.2737 - accuracy: 0.1108\n",
      "Epoch 49/50\n",
      "352/352 [==============================] - 3s 8ms/step - loss: 3.2605 - accuracy: 0.1108\n",
      "Epoch 50/50\n",
      "352/352 [==============================] - 3s 9ms/step - loss: 3.2504 - accuracy: 0.1114\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 5.2107 - accuracy: 0.0591\n",
      "Test accuracy: 0.059090908616781235\n"
     ]
    }
   ],
   "source": [
    "X_encoded=df[['TEMPERATURE','HUMIDITY','SOIL_TYPE']]\n",
    "X_encoded['SOIL_TYPE']=df['SOIL_TYPE'].astype(\"category\").cat.codes\n",
    "Y_encoded =df[['CROP']]\n",
    "Y_encoded=np.array(pd.get_dummies(Y_encoded, columns=['CROP'], prefix='CROP'))\n",
    "\n",
    "x_encoded_train,x_encoded_test, y_encoded_train, y_encoded_test = train_test_split(X_encoded,Y_encoded, test_size = 0.2)\n",
    "x_encoded_train = np.array(x_encoded_train, dtype=np.float32)\n",
    "y_encoded_train = np.array(y_encoded_train, dtype=np.float32)\n",
    "x_encoded_test = np.array(x_encoded_test, dtype=np.float32)\n",
    "y_encoded_test = np.array(y_encoded_test, dtype=np.float32)\n",
    "model = models.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=(X_encoded.shape[1],)),\n",
    "    layers.Dense(320, activation='sigmoid'),\n",
    "    layers.Dense(220, activation='relu'),\n",
    "    layers.Dense(320, activation='relu'),\n",
    "    layers.Dense(149, activation='softmax')  # Output layer with 149 units for 149 classes\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',  # Since your target is encoded\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_encoded_train, y_encoded_train, epochs=50, batch_size=5)\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(x_encoded_test, y_encoded_test)\n",
    "print('Test accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "\n",
    "# Define and train the model\n",
    "forest = RandomForestClassifier(n_estimators=100)\n",
    "multi_target_forest = MultiOutputClassifier(forest, n_jobs=-1)\n",
    "multi_target_forest.fit(x_encoded_train, y_encoded_train)\n",
    "\n",
    "# Predict on test data\n",
    "Y_pred = multi_target_forest.predict(x_encoded_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CROP']=df['CROP'].astype(\"category\")\n",
    "df['CROP_codes']=df['CROP'].cat.codes\n",
    "\n",
    "df['SOIL_TYPE']=df['SOIL_TYPE'].astype(\"category\")\n",
    "df['SOIL_TYPE_codes']=df['SOIL_TYPE'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>SOIL_TYPE</th>\n",
       "      <th>N_SOIL</th>\n",
       "      <th>P_SOIL</th>\n",
       "      <th>K_SOIL</th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>HUMIDITY</th>\n",
       "      <th>ph</th>\n",
       "      <th>RAINFALL</th>\n",
       "      <th>CROP_PRICE</th>\n",
       "      <th>CROP</th>\n",
       "      <th>CROP_codes</th>\n",
       "      <th>SOIL_TYPE_codes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>7000</td>\n",
       "      <td>Amaranthus</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>5000</td>\n",
       "      <td>Green Banana</td>\n",
       "      <td>54</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>7000</td>\n",
       "      <td>Ladies Finger</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>7000</td>\n",
       "      <td>Bitter Gourd</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sandy soil</td>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>120000</td>\n",
       "      <td>Black pepper</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SOIL_TYPE  N_SOIL  P_SOIL  K_SOIL  TEMPERATURE   HUMIDITY        ph  \\\n",
       "0  Sandy soil      90      42      43    20.879744  82.002744  6.502985   \n",
       "1  Sandy soil      85      58      41    21.770462  80.319644  7.038096   \n",
       "2  Sandy soil      60      55      44    23.004459  82.320763  7.840207   \n",
       "3  Sandy soil      74      35      40    26.491096  80.158363  6.980401   \n",
       "4  Sandy soil      78      42      42    20.130175  81.604873  7.628473   \n",
       "\n",
       "     RAINFALL  CROP_PRICE           CROP  CROP_codes  SOIL_TYPE_codes  \n",
       "0  202.935536        7000     Amaranthus           0               14  \n",
       "1  226.655537        5000   Green Banana          54               14  \n",
       "2  263.964248        7000  Ladies Finger          71               14  \n",
       "3  242.864034        7000   Bitter Gourd          13               14  \n",
       "4  262.717340      120000   Black pepper          15               14  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"SOIL_categories.pkl\",'wb') as fl:\n",
    "    pickle.dump(df['SOIL_TYPE'],fl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>N_SOIL</th>\n",
       "      <th>P_SOIL</th>\n",
       "      <th>K_SOIL</th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>HUMIDITY</th>\n",
       "      <th>ph</th>\n",
       "      <th>RAINFALL</th>\n",
       "      <th>CROP_PRICE</th>\n",
       "      <th>CROP_codes</th>\n",
       "      <th>SOIL_TYPE_codes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N_SOIL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.231460</td>\n",
       "      <td>-0.140512</td>\n",
       "      <td>0.026504</td>\n",
       "      <td>0.190688</td>\n",
       "      <td>0.096683</td>\n",
       "      <td>0.059020</td>\n",
       "      <td>-0.001302</td>\n",
       "      <td>0.108717</td>\n",
       "      <td>-0.217312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P_SOIL</th>\n",
       "      <td>-0.231460</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.736232</td>\n",
       "      <td>-0.127541</td>\n",
       "      <td>-0.118734</td>\n",
       "      <td>-0.138019</td>\n",
       "      <td>-0.063839</td>\n",
       "      <td>-0.023683</td>\n",
       "      <td>-0.064485</td>\n",
       "      <td>0.015944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K_SOIL</th>\n",
       "      <td>-0.140512</td>\n",
       "      <td>0.736232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.160387</td>\n",
       "      <td>0.190859</td>\n",
       "      <td>-0.169503</td>\n",
       "      <td>-0.053461</td>\n",
       "      <td>-0.021923</td>\n",
       "      <td>-0.010712</td>\n",
       "      <td>-0.256741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <td>0.026504</td>\n",
       "      <td>-0.127541</td>\n",
       "      <td>-0.160387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205320</td>\n",
       "      <td>-0.017795</td>\n",
       "      <td>-0.030084</td>\n",
       "      <td>-0.029044</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>-0.028574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMIDITY</th>\n",
       "      <td>0.190688</td>\n",
       "      <td>-0.118734</td>\n",
       "      <td>0.190859</td>\n",
       "      <td>0.205320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008483</td>\n",
       "      <td>0.094423</td>\n",
       "      <td>-0.041414</td>\n",
       "      <td>0.034028</td>\n",
       "      <td>-0.410890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ph</th>\n",
       "      <td>0.096683</td>\n",
       "      <td>-0.138019</td>\n",
       "      <td>-0.169503</td>\n",
       "      <td>-0.017795</td>\n",
       "      <td>-0.008483</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109069</td>\n",
       "      <td>-0.033548</td>\n",
       "      <td>0.033992</td>\n",
       "      <td>-0.021919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAINFALL</th>\n",
       "      <td>0.059020</td>\n",
       "      <td>-0.063839</td>\n",
       "      <td>-0.053461</td>\n",
       "      <td>-0.030084</td>\n",
       "      <td>0.094423</td>\n",
       "      <td>-0.109069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058847</td>\n",
       "      <td>0.030660</td>\n",
       "      <td>-0.281774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROP_PRICE</th>\n",
       "      <td>-0.001302</td>\n",
       "      <td>-0.023683</td>\n",
       "      <td>-0.021923</td>\n",
       "      <td>-0.029044</td>\n",
       "      <td>-0.041414</td>\n",
       "      <td>-0.033548</td>\n",
       "      <td>0.058847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118927</td>\n",
       "      <td>0.182653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROP_codes</th>\n",
       "      <td>0.108717</td>\n",
       "      <td>-0.064485</td>\n",
       "      <td>-0.010712</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.034028</td>\n",
       "      <td>0.033992</td>\n",
       "      <td>0.030660</td>\n",
       "      <td>-0.118927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.162522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOIL_TYPE_codes</th>\n",
       "      <td>-0.217312</td>\n",
       "      <td>0.015944</td>\n",
       "      <td>-0.256741</td>\n",
       "      <td>-0.028574</td>\n",
       "      <td>-0.410890</td>\n",
       "      <td>-0.021919</td>\n",
       "      <td>-0.281774</td>\n",
       "      <td>0.182653</td>\n",
       "      <td>-0.162522</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   N_SOIL    P_SOIL    K_SOIL  TEMPERATURE  HUMIDITY  \\\n",
       "N_SOIL           1.000000 -0.231460 -0.140512     0.026504  0.190688   \n",
       "P_SOIL          -0.231460  1.000000  0.736232    -0.127541 -0.118734   \n",
       "K_SOIL          -0.140512  0.736232  1.000000    -0.160387  0.190859   \n",
       "TEMPERATURE      0.026504 -0.127541 -0.160387     1.000000  0.205320   \n",
       "HUMIDITY         0.190688 -0.118734  0.190859     0.205320  1.000000   \n",
       "ph               0.096683 -0.138019 -0.169503    -0.017795 -0.008483   \n",
       "RAINFALL         0.059020 -0.063839 -0.053461    -0.030084  0.094423   \n",
       "CROP_PRICE      -0.001302 -0.023683 -0.021923    -0.029044 -0.041414   \n",
       "CROP_codes       0.108717 -0.064485 -0.010712     0.000355  0.034028   \n",
       "SOIL_TYPE_codes -0.217312  0.015944 -0.256741    -0.028574 -0.410890   \n",
       "\n",
       "                       ph  RAINFALL  CROP_PRICE  CROP_codes  SOIL_TYPE_codes  \n",
       "N_SOIL           0.096683  0.059020   -0.001302    0.108717        -0.217312  \n",
       "P_SOIL          -0.138019 -0.063839   -0.023683   -0.064485         0.015944  \n",
       "K_SOIL          -0.169503 -0.053461   -0.021923   -0.010712        -0.256741  \n",
       "TEMPERATURE     -0.017795 -0.030084   -0.029044    0.000355        -0.028574  \n",
       "HUMIDITY        -0.008483  0.094423   -0.041414    0.034028        -0.410890  \n",
       "ph               1.000000 -0.109069   -0.033548    0.033992        -0.021919  \n",
       "RAINFALL        -0.109069  1.000000    0.058847    0.030660        -0.281774  \n",
       "CROP_PRICE      -0.033548  0.058847    1.000000   -0.118927         0.182653  \n",
       "CROP_codes       0.033992  0.030660   -0.118927    1.000000        -0.162522  \n",
       "SOIL_TYPE_codes -0.021919 -0.281774    0.182653   -0.162522         1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['CROP','SOIL_TYPE']).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['N_SOIL', 'P_SOIL', 'K_SOIL', 'ph', 'RAINFALL', 'CROP_PRICE'],inplace=True)\n",
    "X=df.drop(columns=['CROP','SOIL_TYPE','CROP_codes'],axis=1)\n",
    "X=np.array(X)\n",
    "Y=np.array(df['CROP_codes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test, y_train, y_test = train_test_split(X,Y, test_size = 0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1980,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_dct_model=None\n",
    "best=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07272727272727272"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range (1,100):\n",
    "    dct_model=DecisionTreeClassifier()\n",
    "    dct_model.fit(x_train,y_train)\n",
    "    dct_y_pred=dct_model.predict(x_test)\n",
    "    score =accuracy_score(y_test,dct_y_pred)\n",
    "    if score>best:\n",
    "        best_dct_model=dct_model\n",
    "        best=score\n",
    "\n",
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct_y_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def try_rf(n):\n",
    "    random_forest_model=RandomForestClassifier(n_estimators=n,random_state=42)\n",
    "    random_forest_model.fit(x_train,y_train)\n",
    "    random_forest_y_pred=random_forest_model.predict(x_test)\n",
    "    score =accuracy_score(y_test,random_forest_y_pred)\n",
    "    return random_forest_model,random_forest_y_pred,score\n",
    "\n",
    "best_score = n = 0\n",
    "random_forest_best_model = nn1_y_pred = None\n",
    "for i in range(200, 1000,25):\n",
    "    random_forest_model, y_pred, score = try_rf(i)\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        random_forest_best_model = random_forest_model\n",
    "        random_forest_y_pred = y_pred\n",
    "        n = i\n",
    "print(\"Best Score\", best_score, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score 0.09090909090909091 5\n"
     ]
    }
   ],
   "source": [
    "def try_knn(n_neighbors):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    knn_model.fit(x_train, y_train)\n",
    "    knn_y_pred = knn_model.predict(x_test)\n",
    "    score = accuracy_score(y_test, knn_y_pred)\n",
    "    return knn_model, knn_y_pred, score\n",
    "\n",
    "\n",
    "best_score = n = 0 \n",
    "knn_best_model = nn1_y_pred = None\n",
    "for i in range(1, 100):\n",
    "    knn_model, y_pred, score = try_knn(i)\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        knn_best_model = knn_model\n",
    "        nn1_y_pred = y_pred\n",
    "        n = i\n",
    "print(\"Best Score\", best_score, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"KNN_MODLE.pkl\",'wb') as fl:\n",
    "    pickle.dump(knn_best_model,fl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'Python 3.11.5' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "svm_model_linear = SVC(kernel = 'linear', C = 1).fit(x_train, y_train) \n",
    "svm_predictions = svm_model_linear.predict(x_test) \n",
    "  \n",
    "# model accuracy for X_test   \n",
    "accuracy = svm_model_linear.score(x_test, y_test) \n",
    "  \n",
    "# creating a confusion matrix \n",
    "# cm = confusion_matrix(y_test, svm_predictions) \n",
    "print(\"SVC score\",accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_early = tf.keras.callbacks.EarlyStopping(patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Failed to convert a NumPy array to a Tensor (Unsupported object type float).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[124], line 13\u001b[0m\n\u001b[0;32m      8\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      9\u001b[0m               loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# Since your target is encoded\u001b[39;00m\n\u001b[0;32m     10\u001b[0m               metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_encoded_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_encoded_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m# Evaluate the model\u001b[39;00m\n\u001b[0;32m     16\u001b[0m test_loss, test_acc \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(x_test, y_test)\n",
      "File \u001b[1;32mc:\\Users\\Carron  Muleya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mc:\\Users\\Carron  Muleya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py:102\u001b[0m, in \u001b[0;36mconvert_to_eager_tensor\u001b[1;34m(value, ctx, dtype)\u001b[0m\n\u001b[0;32m    100\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtypes\u001b[38;5;241m.\u001b[39mas_dtype(dtype)\u001b[38;5;241m.\u001b[39mas_datatype_enum\n\u001b[0;32m    101\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEagerTensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: Failed to convert a NumPy array to a Tensor (Unsupported object type float)."
     ]
    }
   ],
   "source": [
    "\n",
    "model = models.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=(X_encoded.shape[1],)),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(149, activation='softmax')  # Output layer with 149 units for 149 classes\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',  # Since your target is encoded\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_encoded_train, y_encoded_train, epochs=50, batch_size=5)\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print('Test accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "99/99 [==============================] - 3s 5ms/step - loss: 4.9813 - accuracy: 0.0359\n",
      "Epoch 2/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.9243 - accuracy: 0.0419\n",
      "Epoch 3/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.8756 - accuracy: 0.0465\n",
      "Epoch 4/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.8297 - accuracy: 0.0465\n",
      "Epoch 5/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.7867 - accuracy: 0.0465\n",
      "Epoch 6/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.7463 - accuracy: 0.0465\n",
      "Epoch 7/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.7085 - accuracy: 0.0465\n",
      "Epoch 8/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.6731 - accuracy: 0.0429\n",
      "Epoch 9/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.6400 - accuracy: 0.0404\n",
      "Epoch 10/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.6091 - accuracy: 0.0444\n",
      "Epoch 11/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.5803 - accuracy: 0.0470\n",
      "Epoch 12/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.5534 - accuracy: 0.0510\n",
      "Epoch 13/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.5284 - accuracy: 0.0510\n",
      "Epoch 14/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.5050 - accuracy: 0.0510\n",
      "Epoch 15/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.4835 - accuracy: 0.0510\n",
      "Epoch 16/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.4633 - accuracy: 0.0510\n",
      "Epoch 17/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.4448 - accuracy: 0.0510\n",
      "Epoch 18/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.4275 - accuracy: 0.0510\n",
      "Epoch 19/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.4115 - accuracy: 0.0510\n",
      "Epoch 20/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.3968 - accuracy: 0.0510\n",
      "Epoch 21/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.3833 - accuracy: 0.0510\n",
      "Epoch 22/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.3708 - accuracy: 0.0510\n",
      "Epoch 23/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.3593 - accuracy: 0.0510\n",
      "Epoch 24/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.3487 - accuracy: 0.0510\n",
      "Epoch 25/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.3390 - accuracy: 0.0510\n",
      "Epoch 26/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.3301 - accuracy: 0.0510\n",
      "Epoch 27/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.3219 - accuracy: 0.0510\n",
      "Epoch 28/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.3144 - accuracy: 0.0510\n",
      "Epoch 29/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.3076 - accuracy: 0.0510\n",
      "Epoch 30/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.3012 - accuracy: 0.0510\n",
      "Epoch 31/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2955 - accuracy: 0.0510\n",
      "Epoch 32/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2902 - accuracy: 0.0510\n",
      "Epoch 33/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2853 - accuracy: 0.0510\n",
      "Epoch 34/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2809 - accuracy: 0.0510\n",
      "Epoch 35/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2769 - accuracy: 0.0510\n",
      "Epoch 36/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2731 - accuracy: 0.0510\n",
      "Epoch 37/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2696 - accuracy: 0.0510\n",
      "Epoch 38/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.2664 - accuracy: 0.0510\n",
      "Epoch 39/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2635 - accuracy: 0.0510\n",
      "Epoch 40/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2609 - accuracy: 0.0510\n",
      "Epoch 41/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2585 - accuracy: 0.0510\n",
      "Epoch 42/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2563 - accuracy: 0.0510\n",
      "Epoch 43/100\n",
      "99/99 [==============================] - 1s 10ms/step - loss: 4.2542 - accuracy: 0.0510\n",
      "Epoch 44/100\n",
      "99/99 [==============================] - 2s 16ms/step - loss: 4.2523 - accuracy: 0.0510\n",
      "Epoch 45/100\n",
      "99/99 [==============================] - 1s 11ms/step - loss: 4.2505 - accuracy: 0.0510\n",
      "Epoch 46/100\n",
      "99/99 [==============================] - 1s 11ms/step - loss: 4.2489 - accuracy: 0.0510\n",
      "Epoch 47/100\n",
      "99/99 [==============================] - 2s 15ms/step - loss: 4.2474 - accuracy: 0.0510\n",
      "Epoch 48/100\n",
      "99/99 [==============================] - 1s 15ms/step - loss: 4.2459 - accuracy: 0.0510\n",
      "Epoch 49/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2447 - accuracy: 0.0510\n",
      "Epoch 50/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.2435 - accuracy: 0.0510\n",
      "Epoch 51/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2424 - accuracy: 0.0510\n",
      "Epoch 52/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2414 - accuracy: 0.0510\n",
      "Epoch 53/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2403 - accuracy: 0.0510\n",
      "Epoch 54/100\n",
      "99/99 [==============================] - 1s 11ms/step - loss: 4.2395 - accuracy: 0.0510\n",
      "Epoch 55/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2386 - accuracy: 0.0510\n",
      "Epoch 56/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2378 - accuracy: 0.0510\n",
      "Epoch 57/100\n",
      "99/99 [==============================] - 1s 10ms/step - loss: 4.2372 - accuracy: 0.0510\n",
      "Epoch 58/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2365 - accuracy: 0.0510\n",
      "Epoch 59/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2358 - accuracy: 0.0510\n",
      "Epoch 60/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2352 - accuracy: 0.0510\n",
      "Epoch 61/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2347 - accuracy: 0.0510\n",
      "Epoch 62/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2340 - accuracy: 0.0510\n",
      "Epoch 63/100\n",
      "99/99 [==============================] - 1s 12ms/step - loss: 4.2336 - accuracy: 0.0510\n",
      "Epoch 64/100\n",
      "99/99 [==============================] - 1s 14ms/step - loss: 4.2332 - accuracy: 0.0510\n",
      "Epoch 65/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2327 - accuracy: 0.0510\n",
      "Epoch 66/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2323 - accuracy: 0.0510\n",
      "Epoch 67/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.2319 - accuracy: 0.0510\n",
      "Epoch 68/100\n",
      "99/99 [==============================] - 2s 16ms/step - loss: 4.2315 - accuracy: 0.0510\n",
      "Epoch 69/100\n",
      "99/99 [==============================] - 2s 17ms/step - loss: 4.2312 - accuracy: 0.0510\n",
      "Epoch 70/100\n",
      "99/99 [==============================] - 2s 18ms/step - loss: 4.2308 - accuracy: 0.0510\n",
      "Epoch 71/100\n",
      "99/99 [==============================] - 1s 10ms/step - loss: 4.2305 - accuracy: 0.0510\n",
      "Epoch 72/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2302 - accuracy: 0.0510\n",
      "Epoch 73/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2299 - accuracy: 0.0510\n",
      "Epoch 74/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2297 - accuracy: 0.0510\n",
      "Epoch 75/100\n",
      "99/99 [==============================] - 1s 7ms/step - loss: 4.2295 - accuracy: 0.0510\n",
      "Epoch 76/100\n",
      "99/99 [==============================] - 1s 10ms/step - loss: 4.2292 - accuracy: 0.0510\n",
      "Epoch 77/100\n",
      "99/99 [==============================] - 1s 12ms/step - loss: 4.2289 - accuracy: 0.0510\n",
      "Epoch 78/100\n",
      "99/99 [==============================] - 1s 12ms/step - loss: 4.2288 - accuracy: 0.0510\n",
      "Epoch 79/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2285 - accuracy: 0.0510\n",
      "Epoch 80/100\n",
      "99/99 [==============================] - 2s 15ms/step - loss: 4.2283 - accuracy: 0.0510\n",
      "Epoch 81/100\n",
      "99/99 [==============================] - 2s 15ms/step - loss: 4.2282 - accuracy: 0.0510\n",
      "Epoch 82/100\n",
      "99/99 [==============================] - 1s 14ms/step - loss: 4.2280 - accuracy: 0.0510\n",
      "Epoch 83/100\n",
      "99/99 [==============================] - 1s 14ms/step - loss: 4.2279 - accuracy: 0.0510\n",
      "Epoch 84/100\n",
      "99/99 [==============================] - 1s 11ms/step - loss: 4.2276 - accuracy: 0.0510\n",
      "Epoch 85/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2275 - accuracy: 0.0510\n",
      "Epoch 86/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2274 - accuracy: 0.0510\n",
      "Epoch 87/100\n",
      "99/99 [==============================] - 1s 13ms/step - loss: 4.2272 - accuracy: 0.0510\n",
      "Epoch 88/100\n",
      "99/99 [==============================] - 1s 12ms/step - loss: 4.2272 - accuracy: 0.0510\n",
      "Epoch 89/100\n",
      "99/99 [==============================] - 1s 11ms/step - loss: 4.2269 - accuracy: 0.0510\n",
      "Epoch 90/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2268 - accuracy: 0.0510\n",
      "Epoch 91/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2267 - accuracy: 0.0510\n",
      "Epoch 92/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.2266 - accuracy: 0.0510\n",
      "Epoch 93/100\n",
      "99/99 [==============================] - 1s 9ms/step - loss: 4.2265 - accuracy: 0.0510\n",
      "Epoch 94/100\n",
      "99/99 [==============================] - 1s 8ms/step - loss: 4.2264 - accuracy: 0.0510\n",
      "Epoch 95/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2263 - accuracy: 0.0510\n",
      "Epoch 96/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.2261 - accuracy: 0.0510\n",
      "Epoch 97/100\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 4.2261 - accuracy: 0.0510\n",
      "Epoch 98/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2260 - accuracy: 0.0510\n",
      "Epoch 99/100\n",
      "99/99 [==============================] - 1s 6ms/step - loss: 4.2259 - accuracy: 0.0510\n",
      "Epoch 100/100\n",
      "99/99 [==============================] - 1s 5ms/step - loss: 4.2259 - accuracy: 0.0510\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 4.2706 - accuracy: 0.0364\n",
      "\n",
      "loss: 4.271\n",
      "\n",
      "accuracy: 0.036\n"
     ]
    }
   ],
   "source": [
    "nn1_model = Sequential()\n",
    "nn1_model.add(Dense(64,activation='relu', input_shape=(X.shape[1],)))\n",
    "nn1_model.add(Dense(320, activation = 'relu'))\n",
    "nn1_model.add(Dense(320, activation = 'relu'))\n",
    "nn1_model.add(Dense(10, activation = 'relu'))\n",
    "nn1_model.add(Dense(149,activation='softmax'))\n",
    "\n",
    "nn1_model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "\n",
    "nn1_model.fit(x_train, y_train, epochs = 100, batch_size = 20)\n",
    "scores = nn1_model.evaluate(x_test, y_test)\n",
    "\n",
    "for i, m in enumerate(nn1_model.metrics_names):\n",
    "    print(\"\\n%s: %.3f\"% (m, scores[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 250 Complete [00h 00m 50s]\n",
      "val_loss: 5.0866169929504395\n",
      "\n",
      "Best val_loss So Far: 4.575686454772949\n",
      "Total elapsed time: 00h 39m 49s\n",
      "14/14 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "# Define the neural network model\n",
    "def build_nn_model(hp):\n",
    "    model =Sequential()\n",
    "    model.add(layers.Dense(hp.Int('units', min_value=32, max_value=128, step=32), activation='relu', input_shape=(X_encoded.shape[1],)))\n",
    "    model.add(layers.Dense(149, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=hp.Float('learning_rate', min_value=1e-6, max_value=0.001, sampling='log')), metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# Hyperparameter tuner\n",
    "tuner = kt.Hyperband(build_nn_model, objective='val_loss', max_epochs=100, seed=1)\n",
    "\n",
    "# Early stopping callback\n",
    "stop_early = tf.keras.callbacks.EarlyStopping(patience=5)\n",
    "\n",
    "# Search for the optimal hyperparameters\n",
    "tuner.search(x_encoded_train, y_encoded_train, epochs=100, validation_split=0.2, callbacks=[stop_early], verbose=1)\n",
    "\n",
    "# Get the best model\n",
    "best_nn_model = tuner.get_best_models()[0]\n",
    "\n",
    "# Evaluate the best model on the test data\n",
    "nn_y_pred_prob = best_nn_model.predict(x_encoded_test)\n",
    "nn_y_pred = np.argmax(nn_y_pred_prob, axis=1)  # Convert predicted probabilities to class labels\n",
    "score = accuracy_score(np.argmax(y_encoded_test, axis=1), nn_y_pred)  # Compute accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'Python 3.11.5' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "score"
   ]
  }
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