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Airline Satisfaction Analysis

EDA and K-Means Clusterization

Time Series Forecasting with LSTM and RNN

Simple Sales Analysis

Scikit-learn Pipelines

Heart Disease Classification Task

The Game of LIFE Clone

The Game of LIFE Clone Clone

The Game of LIFE

The Game of LIFE Clone Clone Clone

This Jupyter Notebook is focused on analyzing customer satisfaction within the airline industry. It is structured to conduct an in-depth Exploratory Data Analysis (EDA) to better understand passenger satisfaction drivers, train classifier models such as K-Nearest Neighbors Classifier, Logistic Regression Classifier, and Random Forest Classifier, and determine the best model for predicting the passenger satisfaction level by evaluating and comparing various classification metrics for each of the models.

This Jupyter Notebook is dedicated to exploring a Customer Segmentation dataset through comprehensive Exploratory Data Analysis (EDA) followed by segmentation using the K-Means clustering algorithm. The goal of this analysis is to uncover insights into customer behaviors and characteristics, which can then be leveraged to inform targeted marketing strategies and improve customer service.

This Google Colab notebook tackles a heart disease classification problem using Random Forest and Grid Search optimization. It begins with Exploratory Data Analysis (EDA) to understand the dataset's characteristics, followed by preprocessing steps and model training with Random Forest. Grid Search optimization is then employed to fine-tune model hyperparameters, resulting in improved classification performance.

This Jupyter Notebook presents an analysis of the Google Play Store dataset, focusing on Exploratory Data Analysis (EDA) and the implementation of Scikit-learn pipelines for preprocessing and modeling. The goal is to explore app attributes within the Google Play Store, identify patterns and data structure, and prepare the data for machine learning tasks using Scikit-learn pipelines.

This Jupyter Notebook conducts a detailed analysis of retail sales data, focusing on Exploratory Data Analysis (EDA) and sales representative performance evaluation. Through data visualization techniques, it identifies sales trends, product performance, and the impact of sales representatives on overall sales. The analysis aims to extract actionable insights for optimizing sales strategies and personnel management in a retail setting.

This Google Colab notebook  presents a comprehensive approach to forecasting tasks using Long Short-Term Memory (LSTM) blocks within a Recurrent Neural Network (RNN) architecture. The notebook begins with Exploratory Data Analysis (EDA) to understand the underlying patterns in the time series data. Subsequently, the LSTM and RNN models are implemented and tested for their performance in predicting future values. The notebook offers insights into the effectiveness of these deep learning techniques for time series forecasting tasks.

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