NAME:SAILAJA DONGA
COMPANY:CODTECH IT SOLUTIONS
ID:CT08DS521
DOMAIN:DATASCIENCE
DURATION:SEPTEMBER TO NOVEMBER

Exploratory Data Analysis Project

Overview

Welcome to the Exploratory Data Analysis (EDA) Project repository! This project is dedicated to uncovering insights, patterns, and trends within our dataset through comprehensive exploratory data analysis techniques. By leveraging various statistical and visualization tools, we aim to provide a clear understanding of the underlying data structure and inform subsequent data-driven decisions.

Objectives

1. Data Cleaning & Preparation: Handle missing values, outliers, and ensure data integrity for accurate analysis.
2. Descriptive Statistics: Summarize key metrics to understand the central tendencies and variability within the data.
3. Data Visualization: Create insightful visualizations (e.g., histograms, scatter plots, heatmaps) to illustrate data distributions and relationships.
4. Correlation Analysis: Identify and analyze correlations between different variables to uncover potential associations.
5. Pattern Recognition: Detect trends, clusters, and anomalies that can inform further investigation or hypothesis generation.

Tools & Technologies

1. Python: Primary programming language used for data manipulation and analysis.
2. Pandas: For efficient data handling and preprocessing.
3. NumPy: To perform numerical operations and handle arrays.
4. Matplotlib & Seaborn: For creating a wide range of static, animated, and interactive visualizations.
5. Google colab: Interactive environment to document the analysis process and visualize results.

Methodology

1. Data Collection: Importing datasets from various sources (CSV, databases, APIs).
2. Data Cleaning: Addressing missing values, correcting data types, and removing duplicates.
3. Exploratory Analysis:
4. Generating summary statistics to understand data distribution.
5. Visualizing data to identify patterns and relationships.
6. Performing correlation analysis to detect interdependencies between variables.
7. Insights & Findings: Documenting key discoveries and potential areas for further analysis or modeling.