Machine Learning Projects

This repository contains my machine learning projects and Kaggle competition notebooks. Each project demonstrates practical applications of ML techniques on real-world datasets.

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Projects

1. Titanic: Machine Learning from Disaster

• Competition: titanic-machine-learning-from-disaster
• Description: A classic Kaggle challenge aimed at predicting passenger survival on the Titanic. The project explores data analysis, feature engineering, and model selection to improve predictive performance.
• Techniques:
  • Data preprocessing and feature engineering
  • Model training using LightGBM, XGBoost, and Random Forest
  • Evaluation using confusion matrix, recall, precision, and F1 score

2. Predicting Road Accident Risk

• Competition: Predicting Road Accident Risk
• Description: This project focuses on predicting the risk of road accidents using historical accident data. The goal is to build a model that identifies high-risk areas and times to improve road safety.
• Techniques:
  • Exploratory Data Analysis (EDA) and data cleaning
  • Handling missing values and encoding categorical variables
  • Model training using Decision Trees and Random Forest
  • Evaluation using metrics such as RMSE and R²

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Requirements

Python 3.x and the following libraries:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, recall_score, precision_score, f1_score, mean_squared_error, r2_score
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
import lightgbm as lgb
import xgboost as xgb
import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)