https://github.com/nishchal-kansara/internship-codsoft
Data Science Projects/Tasks performed During Internship program at CodSoft.
https://github.com/nishchal-kansara/internship-codsoft
codsoft data-science data-visualization datasets eda internship machinelearning project python task
Last synced: 9 months ago
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Data Science Projects/Tasks performed During Internship program at CodSoft.
- Host: GitHub
- URL: https://github.com/nishchal-kansara/internship-codsoft
- Owner: nishchal-kansara
- Created: 2024-12-22T17:03:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-22T18:08:11.000Z (over 1 year ago)
- Last Synced: 2025-01-03T16:37:51.755Z (over 1 year ago)
- Topics: codsoft, data-science, data-visualization, datasets, eda, internship, machinelearning, project, python, task
- Language: Jupyter Notebook
- Homepage: https://nishchal-kansara.web.app/
- Size: 2.86 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Science Internship Projects - CodSoft
Submitted By: Nishchal Kansara
Role: Data Science Intern
Batch: December A91
Internship Program: CodSoft
# Introduction
This repository contains the Data Science projects and tasks completed during my internship at CodSoft. The projects demonstrate my learnings and hands-on experience with various Data Science techniques, tools, and algorithms.
# Projects Overview
1. Titanic Survival Prediction
- This project predicts whether a passenger on the Titanic survived or not based on data like age, gender, ticket class, and fare.
- Logistic Regression & Random Forest Classifier
2. Movie Rating Prediction
- Predict movie ratings based on features like genre, director, and actors.
- Linear Regression & Random Forest Regressor
3. Iris Flower Classification
- Classify iris flowers into species based on sepal and petal measurements.
- K-Nearest Neighbors (KNN) & Decision Tree Classifier
4. Sales Prediction using Python
- Predict sales based on TV, radio, and newspaper advertising expenditures.
- Linear Regression & Support Vector Regression (SVR)
5. Credit Card Fraud Detection
- Identify fraudulent transactions using machine learning algorithms.
- Logistic Regression & XGBoost Classifier
# Tools & Technologies Used
- Python
- Pandas, NumPy
- Matplotlib, Seaborn, Plotly
- Scikit-learn
# Learning Outcomes
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- Model building and evaluation
- Handling imbalanced datasets
- Visualization techniques
# Acknowledgment
I am thankful to CodSoft for providing this internship opportunity and helping me enhance my Data Science skills.
Thank you for reviewing my work!
Nishchal Kansara
Data Science Intern at CodSoft