crop yield prediction using rf and ann with fertilizer .ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
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    "id": "GDdXws5Dgd3S",
    "outputId": "ea5f7d7c-e765-434c-a3cc-1b8209cfffb6"
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   "outputs": [
    {
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     "text": [
      "Saving crop_csv_file.xlsx to crop_csv_file.xlsx\n"
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   "source": [
    "data = pd.read_excel('crop_csv_file.xlsx')"
   ]
  },
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       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>soil moisture</th>\n",
       "      <th>area</th>\n",
       "      <th>Production</th>\n",
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       "      <td>Andaman and Nicobar Islands</td>\n",
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       "      <td>Kharif</td>\n",
       "      <td>Arecanut</td>\n",
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       "      <td>1254.0</td>\n",
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       "      <td>Andaman and Nicobar Islands</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
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       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
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       "      <td>Banana</td>\n",
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       "      <td>55</td>\n",
       "      <td>176.0</td>\n",
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       "      <th>49994</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>JANJGIR-CHAMPA</td>\n",
       "      <td>2006</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Small millets</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>Chhattisgarh</td>\n",
       "      <td>JANJGIR-CHAMPA</td>\n",
       "      <td>2006</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Soyabean</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>JANJGIR-CHAMPA</td>\n",
       "      <td>2006</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Sunflower</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>450.0</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>JANJGIR-CHAMPA</td>\n",
       "      <td>2006</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Urad</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>55</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>JANJGIR-CHAMPA</td>\n",
       "      <td>2006</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Gram</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>216.0</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        State_Name   District_Name  ...    area Production\n",
       "0      Andaman and Nicobar Islands        NICOBARS  ...  1254.0     2000.0\n",
       "1      Andaman and Nicobar Islands        NICOBARS  ...     2.0        1.0\n",
       "2      Andaman and Nicobar Islands        NICOBARS  ...   102.0      321.0\n",
       "3      Andaman and Nicobar Islands        NICOBARS  ...   176.0      641.0\n",
       "4      Andaman and Nicobar Islands        NICOBARS  ...   720.0      165.0\n",
       "...                            ...             ...  ...     ...        ...\n",
       "49994                 Chhattisgarh  JANJGIR-CHAMPA  ...    27.0        7.0\n",
       "49995                 Chhattisgarh  JANJGIR-CHAMPA  ...    27.0       32.0\n",
       "49996                 Chhattisgarh  JANJGIR-CHAMPA  ...   450.0      213.0\n",
       "49997                 Chhattisgarh  JANJGIR-CHAMPA  ...  1778.0      309.0\n",
       "49998                 Chhattisgarh  JANJGIR-CHAMPA  ...   216.0      194.0\n",
       "\n",
       "[49999 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1930,
     "status": "ok",
     "timestamp": 1585562818368,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "v5hLAJX0f3k2",
    "outputId": "57904319-9d59-4f4f-959e-13f8b6818186"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49999 entries, 0 to 49998\n",
      "Data columns (total 10 columns):\n",
      "State_Name       49999 non-null object\n",
      "District_Name    49999 non-null object\n",
      "Crop_Year        49999 non-null int64\n",
      "Season           49999 non-null object\n",
      "Crop             49999 non-null object\n",
      "Temperature      49999 non-null int64\n",
      "humidity         49999 non-null int64\n",
      "soil moisture    49999 non-null int64\n",
      " area            49999 non-null float64\n",
      "Production       49784 non-null float64\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a1N2qylYf3k8"
   },
   "outputs": [],
   "source": [
    "#handling missing data\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3267,
     "status": "ok",
     "timestamp": 1585562826238,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "McCQTB1yf3k_",
    "outputId": "2b003b8b-1091-4960-ba80-b5eb76d738f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49784 entries, 0 to 49998\n",
      "Data columns (total 10 columns):\n",
      "State_Name       49784 non-null object\n",
      "District_Name    49784 non-null object\n",
      "Crop_Year        49784 non-null int64\n",
      "Season           49784 non-null object\n",
      "Crop             49784 non-null object\n",
      "Temperature      49784 non-null int64\n",
      "humidity         49784 non-null int64\n",
      "soil moisture    49784 non-null int64\n",
      " area            49784 non-null float64\n",
      "Production       49784 non-null float64\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2798,
     "status": "ok",
     "timestamp": 1585562827366,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "Sw13otAlf3lD",
    "outputId": "d4f7dd4b-fdf6-469f-e56b-b35672eea843"
   },
   "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>Crop_Year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>soil moisture</th>\n",
       "      <th>area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49784.000000</td>\n",
       "      <td>49784.000000</td>\n",
       "      <td>49784.000000</td>\n",
       "      <td>49784.000000</td>\n",
       "      <td>49784.000000</td>\n",
       "      <td>4.978400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.872087</td>\n",
       "      <td>34.445746</td>\n",
       "      <td>44.775269</td>\n",
       "      <td>53.109312</td>\n",
       "      <td>7393.028993</td>\n",
       "      <td>4.132019e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.108602</td>\n",
       "      <td>3.498277</td>\n",
       "      <td>6.662586</td>\n",
       "      <td>5.259326</td>\n",
       "      <td>27983.253218</td>\n",
       "      <td>1.232609e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>6.384400e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2567.000000</td>\n",
       "      <td>5.615000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>877029.000000</td>\n",
       "      <td>7.801620e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Crop_Year   Temperature  ...           area    Production\n",
       "count  49784.000000  49784.000000  ...   49784.000000  4.978400e+04\n",
       "mean    2005.872087     34.445746  ...    7393.028993  4.132019e+05\n",
       "std        5.108602      3.498277  ...   27983.253218  1.232609e+07\n",
       "min     1997.000000     25.000000  ...       0.200000  0.000000e+00\n",
       "25%     2002.000000     34.000000  ...      78.000000  8.700000e+01\n",
       "50%     2006.000000     36.000000  ...     441.000000  6.384400e+02\n",
       "75%     2010.000000     36.000000  ...    2567.000000  5.615000e+03\n",
       "max     2014.000000     37.000000  ...  877029.000000  7.801620e+08\n",
       "\n",
       "[8 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1074,
     "status": "ok",
     "timestamp": 1585563259322,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "2PJ927PZf3lH",
    "outputId": "14954668-bec6-4247-9814-108718faee81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR! Session/line number was not unique in database. History logging moved to new session 59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "testing = data\n",
    "State_Name = le.fit_transform(data.State_Name)\n",
    "District_Name = le.fit_transform(data.District_Name)\n",
    "Crop_Year = le.fit_transform(data.Crop_Year)\n",
    "crop = le.fit_transform(data.Crop)\n",
    "Season1 = le.fit_transform(data.Season)\n",
    "testing['State_Name'] = State_Name\n",
    "testing['District_Name'] = District_Name\n",
    "testing['Crop_Year'] = Crop_Year\n",
    "testing['Crop'] = crop\n",
    "testing['Season']  = Season1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LZlpRxy3io3y"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "X = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1:]\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.2,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jGC1aFWWf3lP"
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1436,
     "status": "ok",
     "timestamp": 1585563417928,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "LcBsvaTyg9ZC",
    "outputId": "1712b752-fb1d-4f92-9aa9-80cbf4d5498c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1342: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "             beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "             hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "             learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
       "             momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "             power_t=0.5, random_state=1, shuffle=True, solver='lbfgs',\n",
       "             tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "amAqBBIC795p"
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 837,
     "status": "error",
     "timestamp": 1585563543988,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "QqWPdd6R7-PT",
    "outputId": "103ec3fd-2729-4f74-9733-ebd32b94860c"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-42d62120b584>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_regression\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_check_reg_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m         \u001b[0;31m# XXX: Remove the check in 0.23\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_reg_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1331\u001b[0m         \"\"\"\n\u001b[1;32m   1332\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1333\u001b[0;31m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1334\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py\u001b[0m in \u001b[0;36m_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    683\u001b[0m                                          layer_units[i + 1])))\n\u001b[1;32m    684\u001b[0m         \u001b[0;31m# forward propagate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py\u001b[0m in \u001b[0;36m_forward_pass\u001b[0;34m(self, activations)\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_layers_\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m             activations[i + 1] = safe_sparse_dot(activations[i],\n\u001b[0;32m--> 104\u001b[0;31m                                                  self.coefs_[i])\n\u001b[0m\u001b[1;32m    105\u001b[0m             \u001b[0mactivations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintercepts_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     if (sparse.issparse(a) and sparse.issparse(b)\n",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 9 is different from 1)"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWiPRcn9yuGH"
   },
   "source": [
    "# Fertilizer prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pgJrdZKniIuI"
   },
   "outputs": [],
   "source": [
    "data_1 = pd.read_csv('ferlizer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 819,
     "status": "ok",
     "timestamp": 1585563566328,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "9ihfI__ny0FB",
    "outputId": "55cf4d71-3d18-4337-9e50-4b4287c647d7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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</th>\n",
       "      <th>p</th>\n",
       "      <th>k</th>\n",
       "      <th>amt of n</th>\n",
       "      <th>amt of p</th>\n",
       "      <th>amt of k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
       "      <td>93</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>37</td>\n",
       "      <td>39</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>93</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3237</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3238</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3240 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      n  p  k  amt of n  amt of p  amt of k\n",
       "0     3  5  6        64        50        60\n",
       "1     1  4  2        40        46        30\n",
       "2     5  1  5        93        16        32\n",
       "3     3  1  3        63        20        39\n",
       "4     4  6  6        87        37        39\n",
       "...  .. .. ..       ...       ...       ...\n",
       "3235  1  4  2        40        46        30\n",
       "3236  5  1  5        93        16        32\n",
       "3237  3  1  3        63        20        39\n",
       "3238  4  6  6        87        37        39\n",
       "3239  2  1  1        65        19        32\n",
       "\n",
       "[3240 rows x 6 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i5-KStpWy1US"
   },
   "outputs": [],
   "source": [
    "n = data_1.iloc[:,0:1]\n",
    "p = data_1.iloc[:,1:2]\n",
    "k = data_1.iloc[:,2:3]\n",
    "amt_n = data_1.iloc[:,3:4]\n",
    "amt_p = data_1.iloc[:,4:5]\n",
    "amt_k = data_1.iloc[:,5:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QplJs1M9y3TM"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "X_n_train , X_n_test , y_n_train , y_n_test = train_test_split(n,amt_n,test_size=0.2,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cN5ACU_Ky5DJ"
   },
   "outputs": [],
   "source": [
    "X_p_train , X_p_test , y_p_train , y_p_test = train_test_split(n,amt_n,test_size=0.2,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cvVqpmrdzC0m"
   },
   "outputs": [],
   "source": [
    "X_k_train , X_k_test , y_k_train , y_k_test = train_test_split(n,amt_n,test_size=0.2,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1047,
     "status": "ok",
     "timestamp": 1585563574071,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "w-SHB6ZGzEty",
    "outputId": "7d5d210a-1767-40f3-e3ae-01ac4606c949"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1342: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "             beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "             hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "             learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
       "             momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "             power_t=0.5, random_state=1, shuffle=True, solver='lbfgs',\n",
       "             tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "clf_n = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf_n.fit(X_n_train, y_n_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mwllSnkzzGXs"
   },
   "outputs": [],
   "source": [
    "y_n_pred = clf_n.predict(X_n_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1058,
     "status": "ok",
     "timestamp": 1585563590898,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "i8ljaSysz5-t",
    "outputId": "f7183bb5-2c39-4fdc-e2f6-39b9e70a5331"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2742.505238290881"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_n.score(y_n_test,y_n_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2687,
     "status": "ok",
     "timestamp": 1585563614887,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "e42cbXrJ0WZR",
    "outputId": "2c64f112-9f92-4a8b-9311-adf9b2ccacc7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1342: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-2742.505238290881"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_p = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf_p.fit(X_p_train, y_p_train)\n",
    "y_p_pred = clf_n.predict(X_p_test)\n",
    "clf_n.score(y_p_test,y_p_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1462,
     "status": "ok",
     "timestamp": 1585561436157,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "NecBEz850hG1",
    "outputId": "3ba1c662-9884-4155-c158-bbe7dd9757dc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1342: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(X_p_train, y_k_train)\n",
    "y_k_pred = clf_n.predict(x_k_test)\n",
    "clf_n.score(y_k_test,y_k_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 19191,
     "status": "ok",
     "timestamp": 1585561649424,
     "user": {
      "displayName": "XCIENCIA PROJECTS",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhSbiRKs-qal6qFP48hBdKSf67dqnntdycVMfU1=s64",
      "userId": "06995631013121649714"
     },
     "user_tz": -330
    },
    "id": "0ueVoTNR03mg",
    "outputId": "98173bcf-3bdf-4c26-9227-f3bdf3ab496a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter nitrogen:1\n",
      "enter posporus:2\n",
      "enter pottasium:3\n",
      "Amount of nitrogen Fertizer: 43.21675238500447\n",
      "Amount of posporus Fertizer: 55.96910609931379\n",
      "Amount of nitrogen Fertizer: 68.72145981362311\n"
     ]
    }
   ],
   "source": [
    "n_i = float(input('enter nitrogen:'))\n",
    "p_i = float(input('enter posporus:'))\n",
    "k_i = float(input('enter pottasium:'))\n",
    "p_n = clf_n.predict([[n_i]])\n",
    "p_p = clf_n.predict([[p_i]])\n",
    "p_k = clf_n.predict([[k_i]])\n",
    "print('Amount of nitrogen Fertizer:',p_n[0])\n",
    "print('Amount of posporus Fertizer:',p_p[0])\n",
    "print('Amount of nitrogen Fertizer:',p_k[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w_vr3YIg1Hm5"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "crop yield prediction using rf and ann with fertilizer .ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 },
 "nbformat": 4,
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}