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Zofia Zientek 2023-10-17 18:53:52 +02:00
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Ładowanie danych:"
],
"metadata": {
"id": "coWdAJZAPC1C"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bozs99nnO2jv",
"outputId": "4119ebc8-eccf-4574-866c-2502176e0fbd"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
]
}
],
"source": [
"!git clone git://gonito.net/mieszkania5"
]
},
{
"cell_type": "markdown",
"source": [
"Importy:"
],
"metadata": {
"id": "OFaZTYDGQqLQ"
}
},
{
"cell_type": "code",
"source": [
"import csv\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"data = pd.read_table(\"mieszkania5/train/train.tsv\", delimiter='\\t', header=None)\n",
"data.rename(columns={0: 'cena', 1: 'stan', 2: 'czynsz', 3: 'x3', 4: 'cenazam', 5: 'link', 6: 'pietro', 7: 'x7', 8: 'metraz', 9: 'rynek', 10: 'liczba pokoi', 11: 'budynek', 12: 'x12', 13: 'x13', 14: 'x14', 15: 'x15', 16: 'x16', 17: 'x17', 18: 'x18', 19: 'x19', 20: 'x20', 21: 'x21', 22: 'x22', 23: 'x23', 24: 'x24', 25: 'x25'}, inplace=True)\n",
"\n",
"data.drop('x3', inplace=True, axis=1)\n",
"data.drop('cenazam', inplace=True, axis=1)\n",
"data.drop('link', inplace=True, axis=1)\n",
"data.drop('pietro', inplace=True, axis=1)\n",
"data.drop('budynek', inplace=True, axis=1)\n",
"data.drop('x7', inplace=True, axis=1)\n",
"data.drop('x12', inplace=True, axis=1)\n",
"data.drop('x13', inplace=True, axis=1)\n",
"data.drop('x14', inplace=True, axis=1)\n",
"data.drop('x15', inplace=True, axis=1)\n",
"data.drop('x16', inplace=True, axis=1)\n",
"data.drop('x17', inplace=True, axis=1)\n",
"data.drop('x18', inplace=True, axis=1)\n",
"data.drop('x19', inplace=True, axis=1)\n",
"data.drop('x20', inplace=True, axis=1)\n",
"data.drop('x21', inplace=True, axis=1)\n",
"data.drop('x22', inplace=True, axis=1)\n",
"data.drop('x23', inplace=True, axis=1)\n",
"data.drop('x24', inplace=True, axis=1)\n",
"data.drop('x25', inplace=True, axis=1)\n",
"\n",
"data['czynsz'] = data['czynsz'].str.extract('(\\d+)')\n",
"data['stan'] = data['stan'].map({'do zamieszkania': 2, 'do remontu': 1, 'do wykończenia': 2})\n",
"data['rynek'] = data['rynek'].map({'wtórny': 0, 'pierwotny': 1})\n",
"\n",
"data.dropna(inplace=True)"
],
"metadata": {
"id": "K-TUB0pAPCp2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "57zFDlw7PDDb"
}
},
{
"cell_type": "code",
"source": [
"cena = data['cena']\n",
"parametry = data[['stan', 'czynsz', 'liczba pokoi', 'metraz', 'rynek']]"
],
"metadata": {
"id": "___F5VBeco6H"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LinearRegression"
],
"metadata": {
"id": "H1shMEsxTccr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def train_model(cena, parametry):\n",
" model = LinearRegression()\n",
" model.fit(X=parametry, y=cena)\n",
" return model"
],
"metadata": {
"id": "vT9sCZ2XTjKy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = train_model(cena, parametry)"
],
"metadata": {
"id": "-DZ-HNMtUBmr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def predict(stan, czynsz, liczba_pokoi, metraz, rynek):\n",
" return model.predict(np.array([[stan, czynsz, liczba_pokoi, metraz, rynek]])).item()"
],
"metadata": {
"id": "oK_ZW9N9Wg2u"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"predict(1, 200, 2, 40.0, 0)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bLmRBRBMgFTg",
"outputId": "f94f3691-9a2a-4035-b3ad-dde097631e85"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
" warnings.warn(\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"217119.72285625804"
]
},
"metadata": {},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "K7eEdZFzgI3n"
},
"execution_count": null,
"outputs": []
}
]
}