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Kaggle【HousePrices】に挑戦

Last updated at Posted at 2019-11-07

概要

敷地面積、築年数などの特徴量から家の価格を予測するコンペに挑んでみた。

ライブラリ定義

import pandas as pd

学習データ、テストデータの読み込み

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

学習データ、テストデータの分割

目的変数「SalePrice」とそれ以外の従属変数に分離する。

train_x = train.drop(['Id', 'SalePrice'], axis=1)
train_y = train['SalePrice']
test_x = test.drop(['Id'], axis=1)

カテゴリカル変数を数値に置き換える

for column in train_x.columns:
    labels, uniques = pd.factorize(train_x[column])
    train_x[column] = labels
for column in test_x.columns:
    labels, uniques = pd.factorize(test_x[column])
    test_x[column] = labels

線形回帰でフィッティング

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(train_x, train_y)

予測

pred_y = regressor.predict(test_x)

CSV作成

submission = pd.DataFrame({'Id':test['Id'], 'SalePrice':pred_y})
submission.to_csv('submission.csv', index=False)
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