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priyanshubiswas-tech / Customer-churn-analysis-EDA-

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This project analyzes customer churn in a telecom company using Python, Pandas, SQL, and data visualization. It identifies key factors like contract type, payment method, and tenure to provide insights for improving retention. The skills gained are applicable in customer retention, user behavior analysis, fraud detection, and HR analytics.

python numpy pandas-dataframe exploratory-data-analysis data-visualization seaborn dataset data-analysis matplotlib matplotlib-pyplot explo seaborn-python
  • Updated Mar 18, 2025
  • Jupyter Notebook

BodaKalyanSingh / Flight_Prediction_project

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Implemented a flight prediction system using two algorithms, Linear Regression and Decision Trees, to forecast flight prices accurately. The project involved preprocessing and analyzing flight data to extract relevant features such as departure time, destination, and airline. Utilized the Linear Regression algorithm

python machine-learning linear-programming decision-tree-classifier datavisualization explo
  • Updated Mar 15, 2024
  • Jupyter Notebook

hendurhance / gui-apps

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A codebase for two data analysis projects, both developed using Python. The first project is a GUI application for managing student performance, using Tkinter and csv for input storage and analysis. The second project is a web scraping tool for analyzing movie data from IMDB, using bs4 and pandas for data processing and matplotlib for visualization

visualization python exploratory-data-analysis scraping imdb-api datanalysis explo youtubescraper
  • Updated Jan 1, 2023
  • Python

shikhararyan / Heart-attack-risk-Prediction-Model---Random-Forest

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This notebook explores and analyzes the Heart Disease UCI dataset using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn. It includes data visualization, feature engineering, model building using Random Forest Classifier, and evaluation of the model's performance in predicting the presence or absence of heart disease.

python random-forest seaborn prediction-model sklearn-library explo
  • Updated Apr 9, 2024
  • Jupyter Notebook

loreeugene / Property-Investment-Decision

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This project is a hands-on exploration of multiple regression modeling techniques applied to real-world challenges in the context of real estate. It aims to understand how different features influence house or property prices. By utilizing multiple regression models, the project provides insights, decision support, and enhances data science skills.

data-science model linear-regression cross-validation ols multiple test-train-dataset explo test-train-split
  • Updated Sep 29, 2023
  • Jupyter Notebook

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