master #2
212
Mieszkania.ipynb
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212
Mieszkania.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"Ładowanie danych:"
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],
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"metadata": {
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"id": "coWdAJZAPC1C"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bozs99nnO2jv",
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"outputId": "4119ebc8-eccf-4574-866c-2502176e0fbd"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
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]
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}
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],
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"source": [
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"!git clone git://gonito.net/mieszkania5"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Importy:"
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],
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"metadata": {
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"id": "OFaZTYDGQqLQ"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import csv\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"data = pd.read_table(\"mieszkania5/train/train.tsv\", delimiter='\\t', header=None)\n",
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"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",
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"\n",
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"data.drop('x3', inplace=True, axis=1)\n",
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"data.drop('cenazam', inplace=True, axis=1)\n",
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"data.drop('link', inplace=True, axis=1)\n",
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"data.drop('pietro', inplace=True, axis=1)\n",
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"data.drop('budynek', inplace=True, axis=1)\n",
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"data.drop('x7', inplace=True, axis=1)\n",
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"data.drop('x12', inplace=True, axis=1)\n",
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"data.drop('x13', inplace=True, axis=1)\n",
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"data.drop('x14', inplace=True, axis=1)\n",
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"data.drop('x15', inplace=True, axis=1)\n",
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"data.drop('x16', inplace=True, axis=1)\n",
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"data.drop('x17', inplace=True, axis=1)\n",
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"data.drop('x18', inplace=True, axis=1)\n",
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"data.drop('x19', inplace=True, axis=1)\n",
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"data.drop('x20', inplace=True, axis=1)\n",
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"data.drop('x21', inplace=True, axis=1)\n",
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"data.drop('x22', inplace=True, axis=1)\n",
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"data.drop('x23', inplace=True, axis=1)\n",
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"data.drop('x24', inplace=True, axis=1)\n",
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"data.drop('x25', inplace=True, axis=1)\n",
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"\n",
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"data['czynsz'] = data['czynsz'].str.extract('(\\d+)')\n",
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"data['stan'] = data['stan'].map({'do zamieszkania': 2, 'do remontu': 1, 'do wykończenia': 2})\n",
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"data['rynek'] = data['rynek'].map({'wtórny': 0, 'pierwotny': 1})\n",
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"\n",
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"data.dropna(inplace=True)"
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],
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"metadata": {
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"id": "K-TUB0pAPCp2"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"id": "57zFDlw7PDDb"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"cena = data['cena']\n",
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"parametry = data[['stan', 'czynsz', 'liczba pokoi', 'metraz', 'rynek']]"
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],
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"metadata": {
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"id": "___F5VBeco6H"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"from sklearn.linear_model import LinearRegression"
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],
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"metadata": {
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"id": "H1shMEsxTccr"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def train_model(cena, parametry):\n",
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" model = LinearRegression()\n",
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" model.fit(X=parametry, y=cena)\n",
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" return model"
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],
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"metadata": {
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"id": "vT9sCZ2XTjKy"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"model = train_model(cena, parametry)"
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],
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"metadata": {
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"id": "-DZ-HNMtUBmr"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def predict(stan, czynsz, liczba_pokoi, metraz, rynek):\n",
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" return model.predict(np.array([[stan, czynsz, liczba_pokoi, metraz, rynek]])).item()"
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],
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"metadata": {
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"id": "oK_ZW9N9Wg2u"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"predict(1, 200, 2, 40.0, 0)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bLmRBRBMgFTg",
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"outputId": "f94f3691-9a2a-4035-b3ad-dde097631e85"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/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",
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" warnings.warn(\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"217119.72285625804"
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]
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},
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"metadata": {},
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"execution_count": 60
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}
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "K7eEdZFzgI3n"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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