DataScience-and-Data-Analysis-with-python

Iris and Titanic ML projects using scikit-learn

Data Science and Data Analysis with Python

Repository containing all tasks completed during my Data Science/Analysis with Python Internship at Main Crafts Technology.

📋 Internship Overview

This internship follows the standard data science workflow: load → clean → analyze → visualize → conclude. Each task focuses on real-world datasets to build practical skills in Python, Pandas, and data visualization.

📊 Completed Tasks

Task 1: Student Performance Data Analysis

File: TASK1.ipynb | Dataset: UCI Student Performance Dataset Objective: Explore the Student Performance dataset to understand factors influencing final grades of 395 secondary school students.

Requirements Covered:

1. Load Dataset: Read student-mat.csv using pandas with sep=';'
2. Explore & Clean Data: Check missing values .isnull(), remove duplicates, inspect shape & dtypes
3. Analysis Questions:
   • Average final grade G3
   • How many students scored above 15?
   • Is study time correlated with performance?
   • Which gender performs better on average?
4. Visualizations: Histogram of grades, Scatterplot: study time vs grades, Bar chart: male vs female average score
5. Documentation: Explained each step using Markdown in Jupyter Notebook

Task 2: Titanic Dataset Analysis

File: TASK2.ipynb | Dataset: Titanic Dataset from Kaggle Objective: Analyze survival patterns from the Titanic disaster and visualize key insights.

Requirements Covered:

1. Load Dataset: Import Titanic dataset from Kaggle
2. Clean Data: Handle missing values like Age column
3. Answer Questions:
   • Who survived more: males or females?
   • Did passenger class affect survival chances?
   • What was the survival rate by age group?
4. Visualizations: Bar chart of survival by gender, Bar chart of survival by class, Histogram of passenger ages

🛠️ Tech Stack

• Language: Python 3.x
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Environment: Google Colab, Jupyter Notebook, Kaggle

📈 Skills Demonstrated

✔️ Data Loading & Cleaning with Pandas ✔️ Exploratory Data Analysis ✔️ Statistical Analysis & Correlation ✔️ Data Visualization & Storytelling ✔️ Handling CSV delimiters and missing values

🔗 Links

Internship: Main Crafts Technology | https://www.maincrafts.com Author: Gajula Praveena (https://github.com/pravee827)