Week 1 / Day 2: Data Cleaning & Visualization with Pandas

Learning Objectives

Today's focus was on mastering data cleaning and visualization techniques using pandas, matplotlib, and seaborn. The goal was to transform raw data into a clean, analysis-ready format while gaining insights through comprehensive visualizations.

Technical Implementation

Data Loading and Quality Assessment

The session began with loading the wine dataset from the previous day's processed data. When the CSV file was unavailable, we implemented a fallback mechanism using sklearn's built-in wine dataset:

try:
    df = pd.read_csv('wine_data.csv')
    print("Dataset loaded successfully!")
except FileNotFoundError:
    from sklearn.datasets import load_wine
    wine_data = load_wine()
    df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)
    df['target'] = wine_data.target
    df['wine_type'] = ['class_' + str(i) for i in wine_data.target]
    print("Wine dataset loaded directly from sklearn!")

Comprehensive Data Quality Analysis

We conducted a thorough assessment of the dataset:

1. Missing Values Check: Confirmed zero missing values across all features
2. Duplicate Detection: Identified no duplicate rows in the dataset
3. Data Type Analysis: Verified appropriate data types (float64 for numerical features, int64 for target, object for wine_type)
4. Statistical Summary: Generated descriptive statistics including mean, standard deviation, quartiles, and extreme values

Data Cleaning and Preprocessing

The cleaning process involved:

• Removing duplicate entries (none found in this dataset)
• Handling outliers using quantile-based methods
• Ensuring data consistency across all features
• Preparing data for machine learning algorithms

Advanced Visualization Techniques

We implemented multiple visualization approaches:

1. Distribution Analysis: Histograms for all numerical features to understand data distributions
2. Correlation Heatmap: Identified strong correlations between features
3. Feature Relationships: Scatter plots showing relationships between key variables
4. Target Distribution: Box plots demonstrating feature distributions across wine classes

Key Insights from Visualization

The correlation analysis revealed significant relationships:

• Strongest Positive Correlation: total_phenols ↔ flavanoids (0.865)
• Strongest Negative Correlation: flavanoids ↔ target (-0.847)
• Feature-Target Relationships: Multiple features showed strong correlations with the target variable

Machine Learning Preparation

The final step involved:

• Separating features and target variables
• Applying StandardScaler for feature normalization
• Saving the cleaned dataset as 'wine_data_cleaned.csv'

Learning Outcomes

Technical Skills Developed

• Advanced pandas operations for data manipulation
• Comprehensive data quality assessment techniques
• Professional visualization using matplotlib and seaborn
• Correlation analysis and interpretation
• Data preprocessing for machine learning

Best Practices Learned

• Always implement fallback mechanisms for data loading
• Conduct thorough data quality checks before analysis
• Use appropriate visualization types for different data characteristics
• Document data transformation steps for reproducibility
• Save intermediate results for future reference

Next Steps

Tomorrow's focus will be on feature engineering and selection, building upon today's clean dataset to prepare for machine learning model development.

Code Repository

All code and processed data are saved in the ai-sprint project directory, with the cleaned dataset available as 'wine_data_cleaned.csv' for future analysis.