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labour_main_2
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ENJq5GSvy3En",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "d6325614-38cc-4d90-baa8-f35fd860cb7a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Centre Gender Labour Category Labour Type State \\\n",
"0 BUKKAPATINAM M FIELD LABOUR FIELD LABOUR ANDHRA PRADESH \n",
"1 BUKKAPATINAM W FIELD LABOUR FIELD LABOUR ANDHRA PRADESH \n",
"2 BUKKAPATINAM M HERDSMEN HERDSMEN ANDHRA PRADESH \n",
"3 BUKKAPATINAM W HERDSMEN HERDSMEN ANDHRA PRADESH \n",
"4 BUKKAPATINAM M SKILLED \\nLABOUR CARPENTER ANDHRA PRADESH \n",
"\n",
" District Wage(INR) Month Year \n",
"0 ANANTAPUR 216.0 Jul 2015.0 \n",
"1 ANANTAPUR 150.0 Jul 2015.0 \n",
"2 ANANTAPUR 200.0 Jul 2015.0 \n",
"3 ANANTAPUR 200.0 Jul 2015.0 \n",
"4 ANANTAPUR 300.0 Jul 2015.0 "
],
"text/html": [
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" <div id=\"df-1fd39d09-599c-4813-b3de-547e79e3cab3\" class=\"colab-df-container\">\n",
" <div>\n",
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" .dataframe tbody tr th:only-of-type {\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>Centre</th>\n",
" <th>Gender</th>\n",
" <th>Labour Category</th>\n",
" <th>Labour Type</th>\n",
" <th>State</th>\n",
" <th>District</th>\n",
" <th>Wage(INR)</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BUKKAPATINAM</td>\n",
" <td>M</td>\n",
" <td>FIELD LABOUR</td>\n",
" <td>FIELD LABOUR</td>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>ANANTAPUR</td>\n",
" <td>216.0</td>\n",
" <td>Jul</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BUKKAPATINAM</td>\n",
" <td>W</td>\n",
" <td>FIELD LABOUR</td>\n",
" <td>FIELD LABOUR</td>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>ANANTAPUR</td>\n",
" <td>150.0</td>\n",
" <td>Jul</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BUKKAPATINAM</td>\n",
" <td>M</td>\n",
" <td>HERDSMEN</td>\n",
" <td>HERDSMEN</td>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>ANANTAPUR</td>\n",
" <td>200.0</td>\n",
" <td>Jul</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BUKKAPATINAM</td>\n",
" <td>W</td>\n",
" <td>HERDSMEN</td>\n",
" <td>HERDSMEN</td>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>ANANTAPUR</td>\n",
" <td>200.0</td>\n",
" <td>Jul</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BUKKAPATINAM</td>\n",
" <td>M</td>\n",
" <td>SKILLED \\nLABOUR</td>\n",
" <td>CARPENTER</td>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>ANANTAPUR</td>\n",
" <td>300.0</td>\n",
" <td>Jul</td>\n",
" <td>2015.0</td>\n",
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"\n",
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" }\n",
"\n",
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" fill: #174EA6;\n",
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"\n",
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"\n",
" [theme=dark] .colab-df-convert {\n",
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"\n",
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" background-color: #434B5C;\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" document.querySelector('#df-1fd39d09-599c-4813-b3de-547e79e3cab3 button.colab-df-convert');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-1fd39d09-599c-4813-b3de-547e79e3cab3');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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" title=\"Suggest charts\"\n",
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"</svg>\n",
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"<style>\n",
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" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
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"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
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" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-fab06c15-10e1-4eb4-b181-ef2e80b8415c button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
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}
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"# importing the necessary libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"df = pd.read_csv(\"Agriculture_Manual_Daily_Labour_Wage_In_India_2010_2019.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3GNAVyRh1Ipd"
},
"outputs": [],
"source": [
"# Handle missing values\n",
"# For example, impute missing values with the mean\n",
"df.fillna(df.mean(numeric_only=True), inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FrF2R6wnfey0",
"outputId": "dbb0f49d-44d5-48c1-de3d-1cc0abd46f0f"
},
"outputs": [
{
"output_type": "stream",
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"<ipython-input-3-922da66ebd6a>:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[col] = encoder.transform(X[col])\n",
"<ipython-input-3-922da66ebd6a>:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[col] = encoder.transform(X[col])\n",
"<ipython-input-3-922da66ebd6a>:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[col] = encoder.transform(X[col])\n",
"<ipython-input-3-922da66ebd6a>:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[col] = encoder.transform(X[col])\n",
"<ipython-input-3-922da66ebd6a>:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[col] = encoder.transform(X[col])\n"
]
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"X = df[['Gender','Labour Category','State','Labour Type','Year']]\n",
"y = df[['Wage(INR)']]\n",
"\n",
"for col in X:\n",
" encoder = LabelEncoder()\n",
" encoder.fit(X[col].drop_duplicates())\n",
" X[col] = encoder.transform(X[col])"
]
},
{
"cell_type": "code",
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"metadata": {},
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"source": [
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "p5O38AjmALQS"
},
"outputs": [],
"source": [
"# Normalize the features\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"y_normalized = scaler.fit_transform(y)"
]
},
{
"cell_type": "code",
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