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real-estate-project #5

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234 changes: 234 additions & 0 deletions ML_model_.ipynb
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Original file line number Diff line number Diff line change
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ML model .ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "px3FIaPsIBbE"
},
"source": [
"import pandas as pd\n"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "28M0pnrcINm0",
"outputId": "67793889-7b09-404f-aaaf-c0571c169bfb"
},
"source": [
"regressor = pd.read_pickle('HousePricePredictor')\n",
"regressor"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LMz9flPPKGjl"
},
"source": [
"cols=['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms', 'Area Population']"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "icQkIPd0Kfsx"
},
"source": [
"ASK USER INCOME"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ua51aC9KKa8T",
"outputId": "b113bb6b-8bdd-46ed-f718-e55a026d0b01"
},
"source": [
"income= eval(input('enter the income:'))\n",
"house_age = eval(input('enter the house age:'))\n",
"rooms= eval(input('enter the number of rooms:'))\n",
"population =eval(input('enter the population'))\n",
"query=pd.DataFrame({'Avg.Area Income':[income],\n",
" 'Avg.Area House Age':[house_age],\n",
" 'Avg. Area Number of Rooms':[rooms],\n",
" 'Avg Population':[population]\n",
" })\n"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"enter the income:100000\n",
"enter the house age:2\n",
"enter the number of rooms:6\n",
"enter the population10000\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 97
},
"id": "T3_w-5qnNkfS",
"outputId": "694b950b-9480-4847-b6bc-b9861cfe5ede"
},
"source": [
"query"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"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>Avg.Area Income</th>\n",
" <th>Avg.Area House Age</th>\n",
" <th>Avg. Area Number of Rooms</th>\n",
" <th>Avg Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100000</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>10000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Avg.Area Income ... Avg Population\n",
"0 100000 ... 10000\n",
"\n",
"[1 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FkA9N5F9Mskc",
"outputId": "0c3b534d-7868-46d2-baea-89936611c463"
},
"source": [
"regressor.predict(query)[0]"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"733683.1258376497"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T47rbR7UM0cz",
"outputId": "7d8c4232-6189-49ff-cc65-3ae39babaaaf"
},
"source": [
"print('price for the house can be:%.1f$'%(regressor.predict(query)[0]))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"price for the house can be:733683.1$\n"
],
"name": "stdout"
}
]
}
]
}
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