#5¶

Kaggle competition: [link]

Entry by Robin R.P.M. Kras

⭐ 1. Introduction & Overview¶

Your Goal: Predict the price of backpacks given various attributes.

🔹 2. Import Libraries & Set Up¶

In [11]:
# General
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Machine Learning
import xgboost as xg
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_absolute_error, mean_squared_error, r2_score, root_mean_squared_error, roc_auc_score
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.preprocessing import StandardScaler, LabelEncoder
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam
from imblearn.over_sampling import SMOTE
from catboost import CatBoostRegressor

# Feature Importance & Explainability
import shap

# Settings
import warnings
warnings.filterwarnings("ignore")

# Set random seed for reproducibility
SEED = 42
np.random.seed(SEED)

print("Libraries loaded. Ready to go!")

Libraries loaded. Ready to go!
In [12]:
import pandas as pd

🔹 3. Load & Explore Data¶

In [13]:
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
In [14]:
train.head()
Out[14]:
id Brand Material Size Compartments Laptop Compartment Waterproof Style Color Weight Capacity (kg) Price
0 0 Jansport Leather Medium 7.0 Yes No Tote Black 11.611723 112.15875
1 1 Jansport Canvas Small 10.0 Yes Yes Messenger Green 27.078537 68.88056
2 2 Under Armour Leather Small 2.0 Yes No Messenger Red 16.643760 39.17320
3 3 Nike Nylon Small 8.0 Yes No Messenger Green 12.937220 80.60793
4 4 Adidas Canvas Medium 1.0 Yes Yes Messenger Green 17.749338 86.02312
In [15]:
train.describe()
Out[15]:
id Compartments Weight Capacity (kg) Price
count 300000.000000 300000.000000 299862.000000 300000.000000
mean 149999.500000 5.443590 18.029994 81.411107
std 86602.684716 2.890766 6.966914 39.039340
min 0.000000 1.000000 5.000000 15.000000
25% 74999.750000 3.000000 12.097867 47.384620
50% 149999.500000 5.000000 18.068614 80.956120
75% 224999.250000 8.000000 24.002375 115.018160
max 299999.000000 10.000000 30.000000 150.000000
In [16]:
train.dtypes
Out[16]:
id                        int64
Brand                    object
Material                 object
Size                     object
Compartments            float64
Laptop Compartment       object
Waterproof               object
Style                    object
Color                    object
Weight Capacity (kg)    float64
Price                   float64
dtype: object

🔹 4. Data Visualization & EDA¶

In [17]:
float_cols = [col for col in train.columns if train[col].dtype == "float64"]

cols_per_row = 3
num_plots = len(float_cols)
rows = (num_plots // cols_per_row) + (num_plots % cols_per_row > 0) 

fig, axes = plt.subplots(rows, cols_per_row, figsize=(15, 5 * rows)) 
axes = axes.flatten()  

for idx, col in enumerate(float_cols):
    sns.histplot(train[col], bins=50, kde=True, ax=axes[idx])
    axes[idx].set_title(f"Distribution of {col}")

for i in range(idx + 1, len(axes)):
    fig.delaxes(axes[i])

plt.tight_layout()
plt.show()
No description has been provided for this image

All data seems to be evenly distributed

In [18]:
train.head()
Out[18]:
id Brand Material Size Compartments Laptop Compartment Waterproof Style Color Weight Capacity (kg) Price
0 0 Jansport Leather Medium 7.0 Yes No Tote Black 11.611723 112.15875
1 1 Jansport Canvas Small 10.0 Yes Yes Messenger Green 27.078537 68.88056
2 2 Under Armour Leather Small 2.0 Yes No Messenger Red 16.643760 39.17320
3 3 Nike Nylon Small 8.0 Yes No Messenger Green 12.937220 80.60793
4 4 Adidas Canvas Medium 1.0 Yes Yes Messenger Green 17.749338 86.02312
In [19]:
test.isnull().sum()
Out[19]:
id                         0
Brand                   6227
Material                5613
Size                    4381
Compartments               0
Laptop Compartment      4962
Waterproof              4811
Style                   5153
Color                   6785
Weight Capacity (kg)      77
dtype: int64

🔹 5. Feature Engineering¶

In [20]:
train['Weight Capacity (kg)'] = train['Weight Capacity (kg)'].fillna(train['Weight Capacity (kg)'].mean())
test['Weight Capacity (kg)'] = test['Weight Capacity (kg)'].fillna(test['Weight Capacity (kg)'].mean())
In [21]:
test['Brand'] = test['Brand'].fillna("None")
test['Material'] = test['Material'].fillna("None")
test['Size'] = test['Size'].fillna(test['Size'].mode())
test['Laptop Compartment'] = test['Laptop Compartment'].fillna(test['Laptop Compartment'].mode())
test['Waterproof'] = test['Waterproof'].fillna(test['Waterproof'].mode())
test['Style'] = test['Style'].fillna(test['Style'].mode())
test['Color'] = test['Color'].fillna(test['Color'].mode())
In [22]:
x = train.columns.intersection(test.columns)
print(x)

Index(['id', 'Brand', 'Material', 'Size', 'Compartments', 'Laptop Compartment',
       'Waterproof', 'Style', 'Color', 'Weight Capacity (kg)'],
      dtype='object')
In [23]:
from sklearn.preprocessing import LabelEncoder

le_dict = {}  

categorical_labels = ['Brand', 'Material', 'Size', 'Laptop Compartment', 'Waterproof', 'Style', 'Color']

for col in categorical_labels:
    le = LabelEncoder()  
    train[col] = le.fit_transform(train[col])  
    test[col] = test[col].map(lambda x: le.transform([x])[0] if x in le.classes_ else -1)
    le_dict[col] = le  

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[23], line 10
      8 le = LabelEncoder()  
      9 train[col] = le.fit_transform(train[col])  
---> 10 test[col] = test[col].map(lambda x: le.transform([x])[0] if x in le.classes_ else -1)
     11 le_dict[col] = le  

File c:\Users\robkr\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\series.py:4700, in Series.map(self, arg, na_action)
   4620 def map(
   4621     self,
   4622     arg: Callable | Mapping | Series,
   4623     na_action: Literal["ignore"] | None = None,
   4624 ) -> Series:
   4625     """
   4626     Map values of Series according to an input mapping or function.
   4627 
   (...)
   4698     dtype: object
   4699     """
-> 4700     new_values = self._map_values(arg, na_action=na_action)
   4701     return self._constructor(new_values, index=self.index, copy=False).__finalize__(
   4702         self, method="map"
   4703     )

File c:\Users\robkr\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\base.py:921, in IndexOpsMixin._map_values(self, mapper, na_action, convert)
    918 if isinstance(arr, ExtensionArray):
    919     return arr.map(mapper, na_action=na_action)
--> 921 return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)

File c:\Users\robkr\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\algorithms.py:1743, in map_array(arr, mapper, na_action, convert)
   1741 values = arr.astype(object, copy=False)
   1742 if na_action is None:
-> 1743     return lib.map_infer(values, mapper, convert=convert)
   1744 else:
   1745     return lib.map_infer_mask(
   1746         values, mapper, mask=isna(values).view(np.uint8), convert=convert
   1747     )

File lib.pyx:2972, in pandas._libs.lib.map_infer()

Cell In[23], line 10, in <lambda>(x)
      8 le = LabelEncoder()  
      9 train[col] = le.fit_transform(train[col])  
---> 10 test[col] = test[col].map(lambda x: le.transform([x])[0] if x in le.classes_ else -1)
     11 le_dict[col] = le  

File c:\Users\robkr\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\preprocessing\_label.py:134, in LabelEncoder.transform(self, y)
    132 y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)
    133 # transform of empty array is empty array
--> 134 if _num_samples(y) == 0:
    135     return np.array([])
    137 return _encode(y, uniques=self.classes_)

File c:\Users\robkr\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py:364, in _num_samples(x)
    360     except Exception as err:
    361         raise TypeError(message) from err
--> 364 def _num_samples(x):
    365     """Return number of samples in array-like x."""
    366     message = "Expected sequence or array-like, got %s" % type(x)

KeyboardInterrupt:
In [ ]:
train.head()
Out[ ]:
id Brand Material Size Compartments Laptop Compartment Waterproof Style Color Weight Capacity (kg) Price
0 0 1 1 1 7.0 1 0 2 0 11.611723 112.15875
1 1 1 0 2 10.0 1 1 1 3 27.078537 68.88056
2 2 4 1 2 2.0 1 0 1 5 16.643760 39.17320
3 3 2 2 2 8.0 1 0 1 3 12.937220 80.60793
4 4 0 0 1 1.0 1 1 1 3 17.749338 86.02312
In [ ]:
train.shape
Out[ ]:
(300000, 11)
In [ ]:
test.shape
Out[ ]:
(200000, 10)

🔹 6. Model Selection¶

In [ ]:
X = train.drop(columns=['id', 'Price'])
X_test = test.drop(columns=['id'])

y = train['Price']

model = CatBoostRegressor(cat_features=categorical_labels, task_type='GPU', devices='0')
In [ ]:
param_grid = {
    'iterations': [100, 250, 500],  
    'learning_rate': [0.01, 0.05, 0.1],  
    'depth': [4, 6, 8],  
    'l2_leaf_reg': [1, 3, 5],  
}

grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, verbose=1)
grid_search.fit(X, y)

best_params = grid_search.best_params_
best_model = grid_search.best_estimator_

Fitting 3 folds for each of 81 candidates, totalling 243 fits
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In [ ]:
best_result = grid_search.best_score_
print(best_result)

0.002102910378480316
In [ ]:
model = CatBoostRegressor(**best_params, task_type='GPU', devices='0')
model.fit(X, y)
predictions = model.predict(X_test)

output = pd.DataFrame({'id': test.id, 'Price': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!")

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Your submission was successfully saved!
In [ ]:
output.head()
Out[ ]:
id Price
0 300000 82.088523
1 300001 82.281495
2 300002 83.180515
3 300003 81.601450
4 300004 77.884284

🔹 7. XGBoost¶

In [ ]:
model = xg.XGBRegressor()
model.fit(X, y)
predictions = model.predict(X_test)

output = pd.DataFrame({'id': test.id, 'Price': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!")

Your submission was successfully saved!