Practical 14: Real-World Project - Part 1 (Data & Preprocessing)

Objective

Work on a complete real-world machine learning project from data collection to deployment.

Part 1 focuses on data gathering, cleaning, and preprocessing.

Duration

5-6 hours

Prerequisites

• Practicals 1-13 completed
• All ML concepts understood

Project Options

Choose one of these real-world problems:

Option 1: Customer Churn Prediction

• Predict which customers will leave
• Prepare customer behavior data
• Handle imbalanced classes

Option 2: House Price Prediction

• Predict house prices based on features
• Clean real estate data
• Handle missing values and outliers

Option 3: Disease Diagnosis

• Predict disease presence from medical data
• Handle sensitive health information
• Address ethical concerns

Option 4: Sentiment Analysis

• Classify text sentiment
• Preprocess text data
• Handle NLP-specific challenges

📋 Tasks for Part 1

1. Data Collection & Understanding

# Load data
df = pd.read_csv('your_dataset.csv')

# Explore
print(df.head())
print(df.info())
print(df.describe())
print(df.isnull().sum())

2. Data Cleaning

# Handle missing values
df.dropna(subset=['critical_column'], inplace=True)
df.fillna(df.median(), inplace=True)

# Remove duplicates
df.drop_duplicates(inplace=True)

# Handle outliers
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
# ... outlier removal code

3. Feature Engineering

# Create new features
df['new_feature'] = df['col1'] * df['col2']

# Encode categorical variables
df = pd.get_dummies(df, columns=['category_col'])

# Normalize features
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)

4. Data Visualization & Analysis

# Visualizations
df.hist(figsize=(15, 10))
plt.show()

# Correlation matrix
import seaborn as sns
sns.heatmap(df.corr(), annot=True)
plt.show()

📊 Deliverables for Part 1

• Cleaned dataset (CSV)
• Data exploration report with visualizations
• Feature engineering documentation
• Jupyter notebook with all code
• Summary of data characteristics

📊 Learning Outcomes

• Collect and load real-world data
• Perform comprehensive EDA
• Clean and preprocess data
• Engineer meaningful features
• Document data preparation process