<jupyter_start><jupyter_text><NAME><jupyter_code>df=pd.read_hdf("data/car.h5")
df.shape
df.columns<jupyter_output><empty_output><jupyter_text>DUMMY MODEL<jupyter_code>df.select_dtypes(np.number).columns
feats=['car_id']
X=df[feats].values
y=df['price_value'].values

model=DummyRegressor()
model.fit(X,y)
y_pred=model.predict(X)
mae(y,y_pred)
[x for x in df.columns if 'price' in x]
df['price_currency'].value_counts(normalize=True)*100
df=df[df['price_currency'] !="EUR"]
df.shape<jupyter_output><empty_output><jupyter_text>FEATURES<jupyter_code>df.head()

df['param_color'].factorize()[0]
 SUFFIX_CAT='_cat'
 for feat in df.columns:
   if isinstance(df[feat][0], list): continue

   factorized_values=df[feat].factorize()[0]
   if SUFFIX_CAT in feat:
     df[feat]=factorized_values 
   else:
    df[feat + SUFFIX_CAT]=factorized_values
cat_feats = [x for x in df.columns if SUFFIX_CAT in x]
cat_feats = [x for x in cat_feats if "price" not in x]
len(cat_feats)  
X=df[cat_feats].values
y=df["price_value"].values

model=DecisionTreeRegressor(max_depth=5)
scores=cross_val_score(model, X, y, cv=3, scoring="neg_mean_absolute_error")
np.mean(scores)
m=DecisionTreeRegressor(max_depth=5)
m.fit(X,y)

imp=PermutationImportance(m, random_state=0).fit(X,y)
eli5.show_weights(imp, feature_names=cat_feats)<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '376560', 'license': 'MIT', 'n_tokens_mistral': 570, 'n_tokens_neox': 554, 'n_words': 80, 'path': 'day3_simple_model.ipynb', 'repo_name': 'marta-maja/dw_matix_car', 'size': '1343'}