Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1959d86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6aaadf1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5272ce4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Estimated_Insects_Count</th>\n",
       "      <th>Crop_Type</th>\n",
       "      <th>Soil_Type</th>\n",
       "      <th>Pesticide_Use_Category</th>\n",
       "      <th>Number_Doses_Week</th>\n",
       "      <th>Number_Weeks_Used</th>\n",
       "      <th>Number_Weeks_Quit</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop_Damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F00000001</td>\n",
       "      <td>188</td>\n",
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       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>F00000009</td>\n",
       "      <td>448</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <th>8</th>\n",
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Estimated_Insects_Count  Crop_Type  Soil_Type  \\\n",
       "0  F00000001                      188          1          0   \n",
       "1  F00000003                      209          1          0   \n",
       "2  F00000004                      257          1          0   \n",
       "3  F00000005                      257          1          1   \n",
       "4  F00000006                      342          1          0   \n",
       "5  F00000008                      448          0          1   \n",
       "6  F00000009                      448          0          1   \n",
       "7  F00000010                      577          1          0   \n",
       "8  F00000012                      731          0          0   \n",
       "9  F00000020                     1132          1          0   \n",
       "\n",
       "   Pesticide_Use_Category  Number_Doses_Week  Number_Weeks_Used  \\\n",
       "0                       1                  0                0.0   \n",
       "1                       1                  0                0.0   \n",
       "2                       1                  0                0.0   \n",
       "3                       1                  0                0.0   \n",
       "4                       1                  0                0.0   \n",
       "5                       1                  0                NaN   \n",
       "6                       1                  0                NaN   \n",
       "7                       1                  0                0.0   \n",
       "8                       1                  0                0.0   \n",
       "9                       1                  0                0.0   \n",
       "\n",
       "   Number_Weeks_Quit  Season  Crop_Damage  \n",
       "0                  0       1            0  \n",
       "1                  0       2            1  \n",
       "2                  0       2            1  \n",
       "3                  0       2            1  \n",
       "4                  0       2            1  \n",
       "5                  0       2            1  \n",
       "6                  0       2            1  \n",
       "7                  0       1            2  \n",
       "8                  0       2            0  \n",
       "9                  0       1            2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0745ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "Estimated_Insects_Count       0\n",
       "Crop_Type                     0\n",
       "Soil_Type                     0\n",
       "Pesticide_Use_Category        0\n",
       "Number_Doses_Week             0\n",
       "Number_Weeks_Used          9000\n",
       "Number_Weeks_Quit             0\n",
       "Season                        0\n",
       "Crop_Damage                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ed7129f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Estimated_Insects_Count</th>\n",
       "      <th>Crop_Type</th>\n",
       "      <th>Soil_Type</th>\n",
       "      <th>Pesticide_Use_Category</th>\n",
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      ],
      "text/plain": [
       "          ID  Estimated_Insects_Count  Crop_Type  Soil_Type  \\\n",
       "0  F00000002                      188          1          1   \n",
       "1  F00000007                      410          1          1   \n",
       "2  F00000011                      626          1          0   \n",
       "3  F00000013                      731          1          0   \n",
       "4  F00000014                      789          0          0   \n",
       "5  F00000015                      851          0          1   \n",
       "6  F00000016                      851          1          0   \n",
       "7  F00000019                     1056          1          0   \n",
       "8  F00000025                     1576          1          0   \n",
       "9  F00000026                     1576          1          0   \n",
       "\n",
       "   Pesticide_Use_Category  Number_Doses_Week  Number_Weeks_Used  \\\n",
       "0                       1                  0                NaN   \n",
       "1                       1                  0                0.0   \n",
       "2                       1                  0                0.0   \n",
       "3                       1                  0                0.0   \n",
       "4                       1                  0                0.0   \n",
       "5                       1                  0                0.0   \n",
       "6                       1                  0                NaN   \n",
       "7                       1                  0                0.0   \n",
       "8                       1                  0                0.0   \n",
       "9                       1                  0                0.0   \n",
       "\n",
       "   Number_Weeks_Quit  Season  \n",
       "0                  0       2  \n",
       "1                  0       2  \n",
       "2                  0       2  \n",
       "3                  0       2  \n",
       "4                  0       1  \n",
       "5                  0       2  \n",
       "6                  0       3  \n",
       "7                  0       1  \n",
       "8                  0       2  \n",
       "9                  0       1  "
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c6c80fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "Estimated_Insects_Count       0\n",
       "Crop_Type                     0\n",
       "Soil_Type                     0\n",
       "Pesticide_Use_Category        0\n",
       "Number_Doses_Week             0\n",
       "Number_Weeks_Used          5893\n",
       "Number_Weeks_Quit             0\n",
       "Season                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f2c861a",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Estimated_Insects_Count', 'Crop_Type', 'Soil_Type',\n",
    "       'Pesticide_Use_Category', 'Number_Doses_Week', 'Number_Weeks_Used',\n",
    "       'Number_Weeks_Quit', 'Season']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e875f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = train[features], train[['Crop_Damage']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4697384a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3bd8bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Converting the dataset in proper LGB format\n",
    "d_train=lgb.Dataset(X_train, label=y_train)\n",
    "\n",
    "#setting up the parameters\n",
    "params={}\n",
    "params['learning_rate']=0.03\n",
    "params['boosting_type']='gbdt' #GradientBoostingDecisionTree\n",
    "params['objective']='multiclass' #Multi-class target feature\n",
    "params['metric']='multi_logloss' #metric for multi-class\n",
    "params['max_depth']=30\n",
    "params['num_class']=3 #no.of unique values in the target class not inclusive of the end value\n",
    "params[\"min_child_weight\"] = 30\n",
    "params[\"max_depth\"] = 6\n",
    "\n",
    "params[\"verbose\"]= -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c578c387",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training the model\n",
    "clf=lgb.train(params,d_train,700)  #training the model on 100 epocs\n",
    "#prediction on the test dataset\n",
    "y_val_pred=clf.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f6d1cbde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8460124540475654"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val_pred = [np.argmax(line) for line in y_val_pred]\n",
    "accuracy_score(y_val_pred, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a5303e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SyJ5Xdm9F1kdL2iw949wZWArMAL4uqTuApIGStm5BnnOAw4AVEbE21b0v2ZT0nFTGDpL2S/l3l7RHRLwKLJN0dEqXpL1y+d4OnAPcJql3Gjn3iYg/kj0zz59b7ufAzyRtl/IeQjaV/d/p+DO815dfbCKP18imws3MrAMVHoST88hWG5dcBhwoaSFZgKtklFrub8Bc4E9kz2LfAqaSjRwfkrSYLFC1ZDagPtXzgbK0hohYHhFvk41cf5bqvoC0ohoYA5yU0pcAR+QzjogbyNo9nSwg3ippEfAXoMm3PkXEdOBysufbT6bzj4yIl9MpPwLOl1QHrG0im98BZ0h6uHxhlpmZtR9FeK3NxiK9vWoa2Yurr0SVb26PmgFRc/zkamZp1iR/drRtLCTNj4jhjR5zELZKDR8+POrq6jZ8opmZvau5INwZFmZ1KunZ6l2NHDooIl7p6PqUSJoIHF2WfENEnF1EfczMrO0chMukQDuk6HqUS8HWAdfMbCPSWRZmmZmZbXIchM3MzAriIGxmZlYQB2EzM7OCOAibmZkVxEHYzMysIA7CZmZmBXEQNjMzK4iDsJmZWUEchM3MzAriIGxmZlYQf3a0Vaz++QZqJ9xWdDWsi/JXE5q9n0fCZmZmBXEQNjMzK4iDsJmZWUE6LAhLCknn5fZPl3RmlfK+UtJR1cirLN+1khZIWiJpoaTvSerwFy6S9pK0ILf/ZUmrJHVP+4MlLWpFvqMk3VrFqpqZWQt0ZEBZDXxB0vYdWOYGSWpucdqqiBgSEXsAnwI+C/xXx9RsPfXAjpJ6p/2RwKPA3rn9OQXUy8zM2qAjg/AaYArwnfID5SNZSa+n36Mk3SvpFklPSzpH0hhJcyXVS9oll83BkuokPS7psHR9N0mTJM2TtEjSybl8Z0maDjxSSeUj4iVgHPBNZXpKmpbq8bCk0SnvPVL9FqQyB6T0r+TS/zvVrVtq++KUz/v6JpW9DqgD9k1Jw4CLyIIv6fdsSVtLuiKV87CkI5rrh7J7sE+6Zpey9HGpX+vWvtlQSVeZmVmFOnpq9SJgjKQ+LbhmL+AU4GPAV4GBETECmAqMz51XC4wADgUuldQTOAloiIh9gH2Af5W0Uzp/KPDtiBhYaUUi4mmgG/BPwKlZUgwGvgxclco8BTg/IoYAw4HnJH0MOAbYP6WvBcYAQ4D+ETEo5TOtmeJnAyMlbQ2sA2ayfhCeA0wE7k79MxqYlM5vrh+QNBK4FDgiIp4qa/OUiBgeEcO7bdWS22ZmZhvSoe8TjohXJV0NfAtYVeFl8yLiRQBJTwF3pPR6skBTcn0aMT4h6WlgN+AQYM/cKLsPMAB4G5gbEcva0JwDgAsBIuIxSX8FBgL3AxMlfQi4OSKekHQQ2eh1niSALYGXgP8BdpZ0IXBbrm2NmQN8D5hF1idPSfqopB2AXmn/EOBwSaena3oCO26gHz5GNkNxSES80Ib+MDOzFiriwzomAw+x/qhvDWlUnhY+bZE7tjq3vS63v4716x9l5QQgYHxEzMgfkDQKeKOlFZe0M9ko9qWmzomI30p6kGxE/sc09Svgqoj4fiN57gV8mmwE/SXgxCayfoBsFLs/WaAHeA44Nrcv4IsRsbSsjOb64UWyYL034CBsZtaBOnylb0SsAK4nmyIteYZspAhwONC9FVkfLWmz9ExzZ2ApMAP4em4V8cA0PdtiacR5KfCriAiyEemYUr5kI86lKVA/HREXALcAewJ3AUdJ+qd0fj9JH0mL1DaLiJuAH5BNkTcqIl4DngVO4L2gez9wGtlUNam941PQRdLeufSm+mEl2QuGn6agbGZmHaSoj608D/hmbv8y4BZJC4HbacUoFfgbMBfYBjglIt6SNJXsWfFDKTC9DBzZgjy3VPbWoO5ko/VfA79Ixy4GLpFUn46NjYjVkr4EfFXSO8DfgZ9ExApJPwDuSCP9d8ieKa8Cpum9tz29b6RcZjbZc9tn0/79wE94b2X0WWQzDYtSnsuAw8ienzfZDxHxj7SY7U+SToyIB1vQR2Zm1krKBnVmG9ajZkDUHD+56GpYF+XPjrZNlaT5ETG8sWP+Ager2OD+fajzH1Izs6rZ5IOwpO3IntmWOygiXimgPheRLb7KOz8imnv7kpmZdUGbfBBOgXZI0fUoiYhTi66DmZl1DH+Bg5mZWUEchM3MzAriIGxmZlYQB2EzM7OCOAibmZkVxEHYzMysIA7CZmZmBXEQNjMzK4iDsJmZWUEchM3MzAriIGxmZlaQTf6zo61y9c83UDvhtqKr0Wr+Kj0z62w8EjYzMyuIg7CZmVlBHITNzMwK0qYgLGmtpAWSFku6QdJWLby+r6Rv5PY/KOnGDVwzVdLujaSPlfSrlpSfrjtT0ullac9I2r6leTVTxumSHkt9NU/ScRs4f6ykD1arfDMz65zaOhJeFRFDImIQ8DZwSguv7wu8G4Qj4oWIOKq5CyLiaxHxSItrWhBJpwCfAkZExBDgIEAbuGws0K5BWJIX5ZmZFaya09GzgI9K2lrSFZLmSnpY0hEAkvZIaQskLZI0ADgH2CWlTZJUK2lxOr+bpHPTKHuRpPEpfaak4Wn7BEmPS5oL7F+qiKQdJN2URp3zJO1fXtlKpLbcJmlhqscxKX2YpHslzZc0Q1JNM9n8B/D1iHgVICJejYirUj4/TPVbLGmKMkcBw4FrUr9s2VR5kvZJfVPqv1Lf9ZQ0TVJ9ugejU/pYSdMl3Q3cJelqSUfm2ntN6X6ZmVn7q0oQTqOqzwL1wETg7ogYAYwGJknammyUfH4aDQ4HngMmAE+l0fQZZdmOA2qBIRGxJ3BNWZk1wI/Igu8BQH6K+nzglxGxD/BFYGorm/YZ4IWI2CuN9m+X1B24EDgqIoYBVwBnN3axpG2A3hHxdBP5/yoi9kl5bwkcFhE3AnXAmNRXa5opbxpwcjpvbS7fU4GIiMHAl4GrJPVMx4amvA4ELicbdSOpDzASWO89SJLGSaqTVLf2zYYNdJeZmbVEW6ckt5S0IG3PIvujPgc4PPectSewI3A/MFHSh4CbI+IJqdlZ2YOBSyNiDUBErCg7vi8wMyJeBpB0HTAwd+3uufy3kdQrIl5vpJxoovwge1FxnqSfAbdGxCxJg4BBwJ0p/27Ai801pBmjJf0bsBXQD1gC/E/ZObs2Vp6kvmQB/v503m+Bw9L2AWSBm4h4TNJfea9v7iz1ZUTcK+liSTuQvVi5qdTf73ZCxBRgCkCPmgFN9ZWZmbVCW4PwqjQKe5eySPHFiFhadu6jkh4EDgX+KOlkoKkRYlttBnw8It6q4NxXgPLp5N7Ayoh4RdJQ4HPAjyXdBfweWBIR+20o44h4VdLrknYuHw2nkenFwPCIeFbSmWQvWMqpsfJSEG6NN8r2rwa+AhwLnNDKPM3MrBXa4y1KM4DxKRgjae/0e2fg6Yi4ALgF2BN4jSzgNeZO4OTSAiJJ/cqOPwgcKGm7NEV8dO7YHcD40o6kIc3U9z6ykXvvdO4XgIURsVbZCuU3I+I3wCSyqdylwA6S9kvnd5e0RzP5/xS4KE1NI6mXstXRpYC7XFIvIL8gLd8vjZYXESuB1yTtm847Nnf9LGBMOn8g2UxE+YuikiuB0wC60oI3M7ONQXuskD0LmAwskrQZsIxsmvRLwFclvQP8HfhJRKyQNDstKPoTcFEun6lkU6iL0jWXAe++BSkiXkyjx/uBlcCC3LXfIgt8i1Ib76OJldsRsUjZW5v+IimAl4CvpcODyZ5prwPeIVtg9XZaPHVBeo66eWrvkib64xKgFzAvteMd4LyIWCnpMmBx6o95uWuuBC6VtArYjyxAN1beScBlqX73AqWHthcDl0iqJ3umPDYiVjc2/R8R/5D0KPCHJupvZmbtRBF+zNdV5Z9zS5oA1ETEt1uYx1Zkz76HRkSzK6961AyImuMnt7a6hfNnR5tZESTNj4jhjR3ze0W7tkMlfZ/sPv6VtNK5UpIOJltM98sNBWCAwf37UOdAZmZWNZtMEJZ0AlA+SpwdEadWKf+LyL1XOTk/IqZVI//GRMR1wHVtuP7PwEeqVyMzM2uJTSYIp2DYngGxKsHczMw2Hf4CBzMzs4I4CJuZmRXEQdjMzKwgDsJmZmYFcRA2MzMriIOwmZlZQRyEzczMCuIgbGZmVhAHYTMzs4I4CJuZmRXEQdjMzKwgm8xnR1vb1T/fQO2E24quRrP8dYVm1pV4JGxmZlYQB2EzM7OCOAibmZkVxEG4k5A0UdISSYskLZC0b9F1MjOz9uWFWZ2ApP2Aw4ChEbFa0vbAFgVXy8zM2plHwp1DDbA8IlYDRMTyiHhB0jBJ90qaL2mGpBoASf8qaZ6khZJukrRVSj9a0uKUfl9K6ylpmqR6SQ9LGp3Sx0q6WdLtkp6Q9POC2m5mtslyEO4c7gA+LOlxSRdLOlBSd+BC4KiIGAZcAZydzr85IvaJiL2AR4GTUvoPgU+n9MNT2qlARMRg4MvAVZJ6pmNDgGOAwcAxkj5cXjFJ4yTVSapb+2ZDtdttZrZJ83R0JxARr0saBvwLMBq4DvgxMAi4UxJAN+DFdMkgST8G+gK9gBkpfTZwpaTrgZtT2gFkwZyIeEzSX4GB6dhdEdEAIOkR4CPAs2V1mwJMAehRMyCq12ozM3MQ7iQiYi0wE5gpqZ5sBLskIvZr5PQrgSMjYqGkscColMcpaUHXocD8FNibszq3vRb/ezAz61Ceju4EJO0qaUAuaQjZNPMOadEWkrpL2iMd7w28mKasx+Ty2SUiHoyIHwIvAx8GZpXOkTQQ2BFY2s5NMjOzCnjk0zn0Ai6U1BdYAzwJjCObBr5AUh+yezUZWAL8J/AgWaB9kCwoA0xKwVzAXcBC4DHgkjS6XgOMTSuwO6ZlZmbWJEX4MZ9VpkfNgKg5fnLR1WiWPzvazDobSfMjYnhjxzwdbWZmVhBPR1vFBvfvQ51HmmZmVeORsJmZWUEchM3MzAriIGxmZlYQB2EzM7OCOAibmZkVxEHYzMysIA7CZmZmBXEQNjMzK4iDsJmZWUEchM3MzAriIGxmZlYQB2EzM7OC+AscrGL1zzdQO+G2oqvRJH+NoZl1NR4Jm5mZFcRB2MzMrCAOwmZmZgVxEDYzMyuIg3A7kzRR0hJJiyQtkLRvM+dOlbR72n5G0vaNnLNdymeBpL9Lej63v0V7tsXMzKrLq6PbkaT9gMOAoRGxOgXVJgNlRHxtQ3lGxCvAkJT/mcDrEXFuVSpsZmYdyiPh9lUDLI+I1QARsTwiXpB0kKSHJdVLukJSDwBJMyUNb2khknpLWiape9rfprSf8jw/jZQXSxqRztk6lT031eWIJvIeJ6lOUt3aNxta3RFmZvZ+DsLt6w7gw5Iel3SxpAMl9QSuBI6JiMFksxFfb0shEfEaMBMovVH2WODmiHgn7W8VEUOAbwBXpLSJwN0RMQIYDUyStHUjeU+JiOERMbzbVn3aUk0zMyvjINyOIuJ1YBgwDngZuA44GVgWEY+n064CPlGF4qYCJ6TtE4BpuWPXpvrcB2wjqS9wCDBB0gKyAN4T2LEK9TAzswr5mXA7i4i1ZEFupqR64NR2Kme2pFpJo4BuEbE4f7j8dEDAFyNiaXvUx8zMNswj4XYkaVdJA3JJQ4CngFpJH01pXwXurVKRVwO/Zf1RMMAxqT4HAA0R0QDMAMZLUjq2d5XqYGZmFfJIuH31Ai5M079rgCfJpqavBW6QtDkwD7i0SuVdA/w45Z/3lqSHge7AiSntLGAysEjSZsAyspXcZmbWQRyE21FEzAdGNnLoLuB9I8+IGJXbrq0g/zPLkg4AboyIlWXpv4mI08quXUX2fNrMzAriILyRkHQh8Fngc+1VxuD+fajzNxWZmVWNg3AnJmk7slFzuYPSh3a8KyLGN5ZHfnRtZmadi4NwJ5b/dCwzM9v4eHW0mZlZQRyEzczMCuIgbGZmVhAHYTMzs4I4CJuZmRXEQdjMzKwgDsJmZmYFcRA2MzMriIOwmZlZQRyEzczMCuKPrbSK1T/fQO2E24quBgDP+IskzGwj4JGwmZlZQRyEzczMCuIgbGZmVhAHYTMzs4J4YVYVSfpnYDKwD7AS+AdwWkQ8XsUytgPuSrv/DKwFXk77IyLi7WqVZWZm7ctBuEokCfg9cFVEHJvS9gI+ADye9jePiDVtKSciXgGGpPzOBF6PiHPbkqeZmRXD09HVMxp4JyIuLSVExEKgm6RZkqYDj0jqKWmapHpJD0saDSBprKRbJM2U9ISk/6q0YEm9JS2T1D3tb1PaT/mdL2mBpMWSRqRztpZ0haS5qR5HNJH3OEl1kurWvtnQhu4xM7NyHglXzyBgfhPHhgKDImKZpO8BERGDJe0G3CFpYDpvRMrnTWCepNsiom5DBUfEa5JmAocCfwCOBW6OiHeyATpbRcQQSZ8ArkhlTATujogTJfUF5kr6c0S8UZb3FGAKQI+aAVFpZ5iZ2YZ5JNwx5kbEsrR9APAbgIh4DPgrUArCd0bEKxGxCrg5nVupqcAJafsEYFru2LWpvPuAbVLQPQSYIGkBMBPoCezYolaZmVmbeCRcPUuAo5o49kYT6eXKR5oVjzwjYrakWkmjgG4RsXgD+Qr4YkQsrbQMMzOrLo+Eq+duoIekcaUESXsC/1J23ixgTDo+kGz0WQqEn5LUT9KWwJHA7BbW4Wrgt6w/CgY4JpV3ANAQEQ3ADGB8WlCGpL1bWJaZmbWRg3CVREQAnwcOlvSUpCXAT4G/l516MbCZpHrgOmBsRKxOx+YCNwGLgJsqeR5c5hpgW9L0c85bkh4GLgVOSmlnAd2BRamuZ7WwLDMzayNPR1dRRLwAfKmRQ5flznmL957dlnsuIo5sQXlnliUdANwYESvL0n8TEaeVXbsKOLnSsszMrPochDcSki4EPgt8rr3KGNy/D3X+9iIzs6pxEO4kIuJK4Mp8WtmnY+UdlD60I3/9+CbyHVWdGpqZWbU5CHdi+U/HMjOzjY8XZpmZmRXEQdjMzKwgDsJmZmYFcRA2MzMriLLPmDDbMEmv8d6ne3V12wPLi65Elbgtnc/G0g5wW6rhIxGxQ2MHvDraWmJpRAwvuhLVIKnObel8Npa2bCztALelvXk62szMrCAOwmZmZgVxELaWmFJ0BarIbemcNpa2bCztALelXXlhlpmZWUE8EjYzMyuIg7CZmVlBHIStIpI+I2mppCclTSi6PuUkfVjSPZIekbRE0rdTej9Jd0p6Iv3eNqVL0gWpPYskDc3ldXw6/wlJxxfYpm6SHpZ0a9rfSdKDqc7XSdoipfdI+0+m47W5PL6f0pdK+nRB7egr6UZJj0l6VNJ+XfW+SPpO+ve1WNK1knp2lfsi6QpJL0lanEur2n2QNExSfbrmAknqwHZMSv++Fkn6vaS+uWON9nVTf9Oaup/tJiL8459mf4BuwFPAzsAWwEJg96LrVVbHGmBo2u4NPA7sDvwcmJDSJwA/S9ufA/4ECPg48GBK7wc8nX5vm7a3LahN3wV+C9ya9q8Hjk3blwJfT9vfAC5N28cC16Xt3dO96gHslO5htwLacRXwtbS9BdC3K94XoD+wDNgydz/GdpX7AnwCGAoszqVV7T4Ac9O5Std+tgPbcQiwedr+Wa4djfY1zfxNa+p+ttePR8JWiRHAkxHxdES8DfwOOKLgOq0nIl6MiIfS9mvAo2R/NI8gCwKk30em7SOAqyPzANBXUg3waeDOiFgREf8L3Al8puNakpH0IeBQYGraF/BJ4MZ0SnlbSm28ETgonX8E8LuIWB0Ry4Anye5lh5HUh+yP5uUAEfF2RKyki94Xsg842lLS5sBWwIt0kfsSEfcBK8qSq3If0rFtIuKByKLX1bm82r0dEXFHRKxJuw8AH8q1o7G+bvRv2gb+n7ULB2GrRH/g2dz+cymtU0rTfnsDDwIfiIgX06G/Ax9I2021qbO0dTLwb8C6tL8dsDL3hyZfr3frnI43pPM7Q1t2Al4GpqWp9amStqYL3peIeB44F/gbWfBtAObTNe9LSbXuQ/+0XZ5ehBPJRuLQ8nY09/+sXTgI20ZFUi/gJuC0iHg1fyy9Qu/078mTdBjwUkTML7ouVbA52dThJRGxN/AG2bTnu7rQfdmWbGS1E/BBYGuKGY23i65yH5ojaSKwBrim6LpUykHYKvE88OHc/odSWqciqTtZAL4mIm5Oyf9IU2Wk3y+l9Kba1Bnauj9wuKRnyKbJPgmcTzYlWPq893y93q1zOt4HeIXO0ZbngOci4sG0fyNZUO6K9+VgYFlEvBwR7wA3k92rrnhfSqp1H57nvSngfHqHkTQWOAwYk15QQMvb8QpN38924SBslZgHDEirBrcgW2QyveA6rSc9y7kceDQifpE7NB0oreA8Hrgll35cWgX6caAhTcvNAA6RtG0a+RyS0jpMRHw/Ij4UEbVkfX13RIwB7gGOaqItpTYelc6PlH5sWqW7EzCAbPFMh4mIvwPPSto1JR0EPEIXvC9k09Afl7RV+vdWakuXuy85VbkP6dirkj6e+ua4XF7tTtJnyB7fHB4Rb+YONdXXjf5NS/enqfvZPtpz1Zd/Np4fstWSj5OtKJxYdH0aqd8BZFNpi4AF6edzZM947gKeAP4M9EvnC7gotaceGJ7L60SyBRxPAicU3K5RvLc6emeyPyBPAjcAPVJ6z7T/ZDq+c+76iamNS2mn1aoVtGEIUJfuzR/IVtV2yfsC/Ah4DFgM/Jps1W2XuC/AtWTPst8hm6E4qZr3ARie+uUp4FekT2TsoHY8SfaMt/R//9IN9TVN/E1r6n62148/ttLMzKwgno42MzMriIOwmZlZQRyEzczMCuIgbGZmVhAHYTMzs4I4CJuZmRXEQdjMzKwg/x8Os6whvhHMYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi = pd.Series(index = features, data = clf.feature_importance())\n",
    "fi.sort_values(ascending=False)[0:30][::-1].plot(kind = 'barh')\n",
    "# clf is our trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce69b2ef",
   "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>Estimated_Insects_Count</th>\n",
       "      <th>Crop_Type</th>\n",
       "      <th>Soil_Type</th>\n",
       "      <th>Pesticide_Use_Category</th>\n",
       "      <th>Number_Doses_Week</th>\n",
       "      <th>Number_Weeks_Used</th>\n",
       "      <th>Number_Weeks_Quit</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88853</th>\n",
       "      <td>3337</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88854</th>\n",
       "      <td>3516</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88855</th>\n",
       "      <td>3516</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88856</th>\n",
       "      <td>3702</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88857</th>\n",
       "      <td>3895</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88858 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated_Insects_Count  Crop_Type  Soil_Type  Pesticide_Use_Category  \\\n",
       "0                          188          1          0                       1   \n",
       "1                          209          1          0                       1   \n",
       "2                          257          1          0                       1   \n",
       "3                          257          1          1                       1   \n",
       "4                          342          1          0                       1   \n",
       "...                        ...        ...        ...                     ...   \n",
       "88853                     3337          1          0                       2   \n",
       "88854                     3516          1          0                       2   \n",
       "88855                     3516          1          0                       2   \n",
       "88856                     3702          1          0                       2   \n",
       "88857                     3895          1          0                       2   \n",
       "\n",
       "       Number_Doses_Week  Number_Weeks_Used  Number_Weeks_Quit  Season  \n",
       "0                      0                0.0                  0       1  \n",
       "1                      0                0.0                  0       2  \n",
       "2                      0                0.0                  0       2  \n",
       "3                      0                0.0                  0       2  \n",
       "4                      0                0.0                  0       2  \n",
       "...                  ...                ...                ...     ...  \n",
       "88853                 10               12.0                 44       3  \n",
       "88854                 10               20.0                 38       1  \n",
       "88855                 15               40.0                  8       2  \n",
       "88856                 10               25.0                 18       3  \n",
       "88857                 20               37.0                  7       3  \n",
       "\n",
       "[88858 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "726ab19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the dataset in proper LGB format\n",
    "d_train=lgb.Dataset(X, label=y)\n",
    "\n",
    "#setting up the parameters\n",
    "params['learning_rate']=0.03\n",
    "params['boosting_type']='gbdt' #GradientBoostingDecisionTree\n",
    "params['objective']='multiclass' #Multi-class target feature\n",
    "params['metric']='multi_logloss' #metric for multi-class\n",
    "params['max_depth']=30\n",
    "params['num_class']=3 #no.of unique values in the target class not inclusive of the end value\n",
    "params[\"min_child_weight\"] = 30\n",
    "params[\"max_depth\"] = 6\n",
    "\n",
    "params[\"verbose\"]= -1\n",
    "\n",
    "#training the model\n",
    "clf=lgb.train(params,d_train,750)  #training the model on 100 epocs\n",
    "#prediction on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf33b273",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(test[features])\n",
    "y_pred = [np.argmax(line) for line in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "02e03297",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    56132\n",
       "1     3166\n",
       "2       12\n",
       "Name: Crop_Damage, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Crop_Damage'] = y_pred\n",
    "test['Crop_Damage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b6cfda66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Estimated_Insects_Count</th>\n",
       "      <th>Crop_Type</th>\n",
       "      <th>Soil_Type</th>\n",
       "      <th>Pesticide_Use_Category</th>\n",
       "      <th>Number_Doses_Week</th>\n",
       "      <th>Number_Weeks_Used</th>\n",
       "      <th>Number_Weeks_Quit</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop_Damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>F00000002</td>\n",
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       "      <td>F00000011</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F00000013</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F00000014</td>\n",
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       "      <th>5</th>\n",
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       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F00000016</td>\n",
       "      <td>851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F00000019</td>\n",
       "      <td>1056</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F00000025</td>\n",
       "      <td>1576</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F00000026</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Estimated_Insects_Count  Crop_Type  Soil_Type  \\\n",
       "0  F00000002                      188          1          1   \n",
       "1  F00000007                      410          1          1   \n",
       "2  F00000011                      626          1          0   \n",
       "3  F00000013                      731          1          0   \n",
       "4  F00000014                      789          0          0   \n",
       "5  F00000015                      851          0          1   \n",
       "6  F00000016                      851          1          0   \n",
       "7  F00000019                     1056          1          0   \n",
       "8  F00000025                     1576          1          0   \n",
       "9  F00000026                     1576          1          0   \n",
       "\n",
       "   Pesticide_Use_Category  Number_Doses_Week  Number_Weeks_Used  \\\n",
       "0                       1                  0                NaN   \n",
       "1                       1                  0                0.0   \n",
       "2                       1                  0                0.0   \n",
       "3                       1                  0                0.0   \n",
       "4                       1                  0                0.0   \n",
       "5                       1                  0                0.0   \n",
       "6                       1                  0                NaN   \n",
       "7                       1                  0                0.0   \n",
       "8                       1                  0                0.0   \n",
       "9                       1                  0                0.0   \n",
       "\n",
       "   Number_Weeks_Quit  Season  Crop_Damage  \n",
       "0                  0       2            1  \n",
       "1                  0       2            1  \n",
       "2                  0       2            1  \n",
       "3                  0       2            1  \n",
       "4                  0       1            1  \n",
       "5                  0       2            1  \n",
       "6                  0       3            1  \n",
       "7                  0       1            1  \n",
       "8                  0       2            1  \n",
       "9                  0       1            1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "781d71ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = pd.DataFrame(test, columns = ['ID', 'Crop_Damage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eb7eb234",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss.to_csv(\"soln.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c05395aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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