Project Title

Mobile Price Classification using Machine Learning Models

Description

This project involves using various machine learning models to classify mobile phones into different price ranges based on their features. We utilize popular libraries such as pandas for data manipulation and scikit-learn for model training and evaluation. The models used include SVC, DecisionTreeClassifier, RandomForestClassifier, and GradientBoostingClassifier. The dataset used is provided in 'train.csv'.

Getting Started

Dependencies

• Python 3.x
• pandas
• matplotlib
• scikit-learn

Installing

1. Clone the repository or download the project files.
2. Ensure you have the required libraries installed. You can install them using pip:

   pip install pandas matplotlib scikit-learn
   

Executing program

1. Load the dataset:

   import pandas as pd
   df = pd.read_csv('train.csv')

2. Print out the first 5 rows of the dataset:

   df.head()

3. Find the unique values in the price range:

   df['price_range'].unique()

4. Check for null values:

   print(df.isnull().sum())

5. Data Preprocessing:

   from sklearn.model_selection import train_test_split
   
   X = df.drop(columns=['price_range'])
   y = df.iloc[:, -1]
   
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

6. Get the description of each column in the training data:

   X_train.describe()

7. Train and evaluate models:

   from sklearn.metrics import classification_report
   from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
   from sklearn.svm import SVC
   from sklearn.tree import DecisionTreeClassifier
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.ensemble import GradientBoostingClassifier
   import matplotlib.pyplot as plt
   
   def evaluationTest(model):
       y_pred = model.predict(X_test)
       print(classification_report(y_test, y_pred))
   
       fig, ax = plt.subplots(figsize=(8, 5))
       cmp = ConfusionMatrixDisplay(confusion_matrix(y_test, y_pred), display_labels=["class_0", "class_1", "class_2", "class_3"])
       cmp.plot(ax=ax)
       plt.show()
   
   models = {
       'Decision Tree': DecisionTreeClassifier(),
       'Random Forest': RandomForestClassifier(),
       'Support Vector Machine': SVC(kernel='linear', gamma=0.5, C=1.0),
       'Gradient Boosting': GradientBoostingClassifier(),
   }
   
   for name, model in models.items():
       model.fit(X_train, y_train)
       print(name)
       evaluationTest(model)