Jupyter Notebook Analysis: "Titanic In-Depth Analysis and KNN from Scratch"

Mirror from Kaggle: Titanic In-Depth Analysis and KNN from Scratch

Focus: K-Nearest Neighbors (KNN) Algorithm from Scratch

Overview

This Jupyter Notebook provides an in-depth analysis of the Titanic dataset and demonstrates the implementation of the K-Nearest Neighbors (KNN) algorithm from scratch. It includes data loading, exploration, visualization, preprocessing, model building, and submission.

🛬 Import Section

• Libraries: sqrt (math), numpy, pandas, os, matplotlib.pyplot, seaborn, wordcloud, sklearn, scipy.
• Purposes: Data manipulation, statistical analysis, visualization, machine learning, and distance calculations.

💾 Load Data

• Data Source: Kaggle's Titanic dataset.
• Files: train.csv, test.csv, gender_submission.csv.
• Dataframes: df_train and df_test.

👀 Data Exploration

• Examining data structure, types, and initial insights.
• Key observations on features like Pclass, Sex, Ticket, Cabin, Embarked, etc.

📝 Key Observations

• Analysis of features' value ranges, uniqueness, and potential transformations.
• Categorical feature handling: Mapping and one-hot encoding.

📊 Data Visualization

• Exploratory visualizations for features like Survived, Sex, Pclass, Age, SibSp, Parch, Fare, and Embarked.
• Correlation matrix to understand feature relationships.

⚙️ Pre Processing

• Imputing missing values and handling NaNs.
• Normalizing and scaling numeric data.
• One-hot encoding of categorical data.
• Preparing train and test datasets for the model.

🧰 Model: K-Nearest Neighbors (KNN)

• Custom implementation of KNN algorithm.
• Euclidean distance calculation.
• Vectorized approach for performance optimization.
• Finding the best k value through brute force.

📨 Submission

• Preparing the submission file based on the KNN model predictions.