Crops Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b0fe4c2-434e-45a8-8f11-d4a7ede3c656",
   "metadata": {},
   "source": [
    "# Crops Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbf0108b-9be2-4af9-8595-656edcae8ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler,PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db84eb83-29c6-4b63-a915-f7cc98100efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('yield.csv')\n",
    "df1=pd.read_csv('rainfall.csv')\n",
    "df3=pd.read_csv('pesticides.csv')\n",
    "df4=pd.read_csv('temp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb6265f2-70a4-40e8-852c-d65aa19608f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56717, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA of yield\n",
    "df.rename(columns={'Value':'hg/ha_yield','Area':'area','Year':'year','Item':'item'},inplace=True)\n",
    "df=df.drop(['Year Code','Element Code','Element','Year Code','Area Code','Domain Code','Domain','Unit','Item Code'],axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f32e61d6-3802-4cfd-9394-00fbfe0438a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(5947, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA for rainfall\n",
    "df1.rename(columns={'average_rain_fall_mm_per_year':'average_rainfall',' Area':'area','Year':'year'},inplace=True)\n",
    "df1['average_rainfall']=pd.to_numeric(df1['average_rainfall'],errors='coerce')\n",
    "df1=df1.dropna()\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "440da91a-d3a6-4433-83d9-2e4c04812312",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(25385, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main=pd.merge(df,df1,on=['year','area'])\n",
    "df_main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c41f8d5-5ca2-40f9-ae54-186421bea34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDA for Pesticides\n",
    "df3.rename(index=str,columns={'Area':'area','Year':'year','Value':'presticides_tonnes'},inplace=True)\n",
    "df3=df3.drop(columns=['Domain','Element','Item','Unit'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fc66619-a43f-490c-bc49-114081ec2563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18949, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main=pd.merge(df_main,df3,on=['area','year'])\n",
    "df_main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7b658b7-f86e-4018-8ace-7b05afecf353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68764, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDA for Average Temperature\n",
    "df4.shape\n",
    "df4=df4.dropna()\n",
    "df4=df4.rename(index=str,columns={'year':'year','country':'area'})\n",
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "602e0efb-46a9-429a-9d6b-6cbfacb517f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28242, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main=pd.merge(df_main,df4,on=['area','year'])\n",
    "df_main.shape\n",
    "# df_main.isnull().sum()\n",
    "# df_main.groupby('item').count()\n",
    "# df_main['area'].nunique()# Total 101 Nation here\n",
    "# df_main.groupby(['area'],sort=True)['hg/ha_yield'].sum().nlargest(10)\n",
    "# df_main.groupby(['item','area'],sort=True)['hg/ha_yield'].sum().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "355ae325-790f-4033-86cb-b3fa993e8fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# corr_data=df_main.select_dtypes(include=['int64','float64']).corr()\n",
    "# mask=np.zeros_like(corr_data,dtype=bool)\n",
    "# mask[np.triu_indices_from(mask)]=True\n",
    "# f,ax=plt.subplots(figsize=(11,9))\n",
    "# cmap=sns.palette='vlag'\n",
    "# sns.heatmap(corr_data,mask=mask,cmap=cmap,vmax=.3,center=0,square=True,linewidths=.5,cbar_kws={'shrink':.5})\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abae52a9-3233-46e1-8595-92fa7532af02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1430c04b-5dd4-4187-96e5-7cb4aa37fa38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final=df_main.drop(columns=['year'],axis=1)\n",
    "df_final=pd.get_dummies(df_final,columns=['area','item'],prefix=['Country','Item'])\n",
    "X=df_final.loc[:,df_final.columns!='hg/ha_yield'].values\n",
    "y=df_final['hg/ha_yield'].values\n",
    "sc=MinMaxScaler()\n",
    "X=sc.fit_transform(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "df_final=df_final.drop(['hg/ha_yield'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc41d9bd-504d-4339-8d9b-33248cba8b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9489387010870032\n",
      "0.9440637663349952\n"
     ]
    }
   ],
   "source": [
    "pf=PolynomialFeatures(degree=2)\n",
    "X_new_train=pf.fit_transform(X_train)\n",
    "X_new_test=pf.fit_transform(X_test)\n",
    "model=SGDRegressor()\n",
    "model.fit(X_new_train,y_train)\n",
    "y_pred_test=model.predict(X_new_test)\n",
    "y_pred_train=model.predict(X_new_train)\n",
    "test_score=r2_score(y_test,y_pred_test)\n",
    "train_score=r2_score(y_train,y_pred_train)\n",
    "print(train_score)\n",
    "print(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73fa5597-b02b-4232-ae47-d427df562e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "335.83"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Albania\tSoybeans\t1990\t7000\t1485.0\t121.00\t16.37\n",
    "input=['Albania','Soybeans',1485.0,121.00,16.37]\n",
    "categorical_col=input[:2]\n",
    "input_df=pd.DataFrame({'average_rainfall':input[2],'presticides_tonnes':input[3],'avg_temp':input[4]},index=[0])\n",
    "input_df1=df_final.head(1)\n",
    "input_df1=input_df1.iloc[:,3:]\n",
    "def update_columns(df, true_columns):\n",
    "    df[true_columns] = True\n",
    "    other_columns = df.columns.difference(true_columns)\n",
    "    df[other_columns] = False\n",
    "    return df\n",
    "true_columns = [f'Country_{categorical_col[0]}',f'Item_{categorical_col[1]}']\n",
    "input_df2= update_columns(input_df1, true_columns)\n",
    "final_df=pd.concat([input_df,input_df2],axis=1)\n",
    "test_input=sc.transform(final_df.values)\n",
    "test_input1=pf.transform(test_input)\n",
    "z=model.predict(test_input1)\n",
    "h=(int(((z[0]/100)*2.47105) * 100) / 100)\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4772d1fb-57f9-4647-974e-d4c0e200eb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('test.csv')\n",
    "df_main.to_csv('main.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e19a663-4494-47b0-84e3-6b38a789227b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pf.pkl']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'model.pkl')\n",
    "joblib.dump(sc,'sc.pkl')\n",
    "joblib.dump(pf,'pf.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c718f4dc-fc73-4de4-a2a2-7b4f501494a7",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>Country_Austria</th>\n",
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       "      <th>Item_Cassava</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>28241</th>\n",
       "      <td>657.0</td>\n",
       "      <td>2550.07</td>\n",
       "      <td>19.76</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28242 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rainfall  presticides_tonnes  avg_temp  Country_Albania  \\\n",
       "0                1485.0              121.00     16.37             True   \n",
       "1                1485.0              121.00     16.37             True   \n",
       "2                1485.0              121.00     16.37             True   \n",
       "3                1485.0              121.00     16.37             True   \n",
       "4                1485.0              121.00     16.37             True   \n",
       "...                 ...                 ...       ...              ...   \n",
       "28237             657.0             2550.07     19.76            False   \n",
       "28238             657.0             2550.07     19.76            False   \n",
       "28239             657.0             2550.07     19.76            False   \n",
       "28240             657.0             2550.07     19.76            False   \n",
       "28241             657.0             2550.07     19.76            False   \n",
       "\n",
       "       Country_Algeria  Country_Angola  Country_Argentina  Country_Armenia  \\\n",
       "0                False           False              False            False   \n",
       "1                False           False              False            False   \n",
       "2                False           False              False            False   \n",
       "3                False           False              False            False   \n",
       "4                False           False              False            False   \n",
       "...                ...             ...                ...              ...   \n",
       "28237            False           False              False            False   \n",
       "28238            False           False              False            False   \n",
       "28239            False           False              False            False   \n",
       "28240            False           False              False            False   \n",
       "28241            False           False              False            False   \n",
       "\n",
       "       Country_Australia  Country_Austria  ...  Item_Cassava  Item_Maize  \\\n",
       "0                  False            False  ...         False        True   \n",
       "1                  False            False  ...         False       False   \n",
       "2                  False            False  ...         False       False   \n",
       "3                  False            False  ...         False       False   \n",
       "4                  False            False  ...         False       False   \n",
       "...                  ...              ...  ...           ...         ...   \n",
       "28237              False            False  ...         False       False   \n",
       "28238              False            False  ...         False       False   \n",
       "28239              False            False  ...         False       False   \n",
       "28240              False            False  ...         False       False   \n",
       "28241              False            False  ...         False       False   \n",
       "\n",
       "       Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  \\\n",
       "0                          False          False             False   \n",
       "1                          False           True             False   \n",
       "2                          False          False              True   \n",
       "3                          False          False             False   \n",
       "4                          False          False             False   \n",
       "...                          ...            ...               ...   \n",
       "28237                      False          False              True   \n",
       "28238                      False          False             False   \n",
       "28239                      False          False             False   \n",
       "28240                      False          False             False   \n",
       "28241                      False          False             False   \n",
       "\n",
       "       Item_Sorghum  Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
       "0             False          False                False       False      False  \n",
       "1             False          False                False       False      False  \n",
       "2             False          False                False       False      False  \n",
       "3              True          False                False       False      False  \n",
       "4             False           True                False       False      False  \n",
       "...             ...            ...                  ...         ...        ...  \n",
       "28237         False          False                False       False      False  \n",
       "28238          True          False                False       False      False  \n",
       "28239         False           True                False       False      False  \n",
       "28240         False          False                 True       False      False  \n",
       "28241         False          False                False        True      False  \n",
       "\n",
       "[28242 rows x 114 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc594e6-3211-4842-967d-cfbe11214b3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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