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from scipy.stats import skew | |
skewed_features = train_df[numerical_features].apply(lambda x: skew(x.dropna().astype(float))) | |
skewed_features = skewed_features[skewed_features > 0.75] | |
skewed_features = skewed_features.index | |
train_df[skewed_features] = np.log1p(train_df[skewed_features]) | |
test_df[skewed_features] = np.log1p(test_df[skewed_features]) |
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from sklearn.preprocessing import StandardScaler | |
scaling = StandardScaler() | |
scaling.fit(train_df[numerical_features]) | |
scaling_result = scaling.transform(train_df[numerical_features]) | |
for i, col in enumerate(numerical_features): | |
train_df[col] = scaling_result[:, i] | |
scaling_result = scaler.transform(test_df_munged[numeric_features]) | |
for i, col in enumerate(numeric_features): |
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train_df["MasVnrArea"].fillna(0, inplace=True) |
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all_df["LotFrontage"] = df["LotFrontage"] | |
for key, group in lot_frontage_by_neighborhood: | |
idx = (df["Neighborhood"] == key) & (df["LotFrontage"].isnull()) | |
all_df.loc[idx, "LotFrontage"] = group.median() |
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all_df["CentralAir"] = (df["CentralAir"] == "Y") * 1.0 |
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quality_dict = {None: 0, "Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5} | |
train_df["ExterQual"] = df["ExterQual"].map(quality_dict).astype(int) | |
train_df["ExterCond"] = df["ExterCond"].map(quality_dict).astype(int) | |
train_df["BsmtQual"] = df["BsmtQual"].map(quality_dict).astype(int) | |
train_df["BsmtCond"] = df["BsmtCond"].map(quality_dict).astype(int) | |
train_df["HeatingQC"] = df["HeatingQC"].map(quality_dict).astype(int) | |
train_df["KitchenQual"] = df["KitchenQual"].map(quality_dict).astype(int) | |
train_df["FireplaceQu"] = df["FireplaceQu"].map(quality_dict).astype(int) | |
train_df["GarageQual"] = df["GarageQual"].map(quality_dict).astype(int) |
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dummies = pd.get_dummies(train_df[column_name], prefix = "_" + column_name) | |
train_df = train_df.join(dummies) | |
train_df = train_df.drop([column_name], axis=1) |
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train_df["Is_Electrical_SBrkr"] = (df["Electrical"] == "SBrkr") * 1 |
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train_df["Aggregate_OverallQual"] = train_df.OverallQual.replace( | |
{1 : 1, 2 : 1, 3 : 1, 4 : 2, 5 : 2, 6 : 2, 7 : 3, 8 : 3, 9 : 3, 10 : 3}) |
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train_df["New_House"] = (train_df["YearRemodAdd"] == train_df["YrSold"]) * 1 |
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