This repository contains the code and documentation for weekly tasks assigned during my internship at Main Flow Services and Technologies. Each task demonstrates a specific aspect of data science using Python.
Familiarized with Python’s fundamental data structures such as lists, dictionaries, tuples, and sets, and explored their practical applications in data science.
Learned to handle and manipulate data using the Pandas library. Tasks included data cleaning, filtering, merging, and aggregation to prepare datasets for analysis.
Explored various data visualization techniques using Matplotlib and Seaborn. Created different types of plots (e.g., line, bar, scatter, histogram) to visually represent data and insights.
Performed exploratory data analysis (EDA) on datasets to uncover patterns, relationships, and insights. Utilized descriptive statistics and visualizations to understand data distributions and correlations.
Engaged in feature engineering to create meaningful variables from raw data and feature selection to identify the most relevant features for modeling. Employed correlation analysis.
Revision of all previous tasks to refine and enhance the overall analysis. This involved re-evaluating exploratory data analysis (EDA), feature engineering, selection processes, and applying advanced techniques to ensure robustness and accuracy.
Each task folder includes detailed comments and documentation for clarity.
This README provides a brief overview of each week's focus, offering insight into the skills and concepts covered during the internship.