👋 Welcome to EDA_Projects_Python's Repository!
I'm passionate about unraveling stories hidden in data! As a data enthusiast, I thrive on exploring and analyzing datasets to extract meaningful insights.
📊 Exploratory Data Analysis (EDA): Dive into my collection of EDA projects, where I leverage Python and its libraries like NumPy, pandas, Matplotlib, Seaborn and other powerful tools to analyse datasets, visualize trends, and unearth valuable patterns.
Understanding the nuances of data is crucial for informed decision-making. EDA not only transforms raw data into valuable insights but also paints a vivid picture of the underlying narrative.
Here's a quick glance at the key steps involved:
Step 1- Dataset Selection I have selected a real-world dataset from Kaggle. The chosen datasets are in CSV format to facilitate easy loading and analysis.
Step 2- Data Preparation and Cleaning In this phase, the dataset is loaded into a notebook by utilizing the the pandas library. The initial exploration encompasses various aspects, including examining the dataset's dimensions (rows and columns), inspecting value ranges and data types to gain insights into its structure, and checking for missing or null values, duplicates, errors and inconsistent data. Additionally, this stage involves addressing missing values through imputation, correcting inaccurate or invalid data, and implementing actions such as typecasting, adding new columns, merging and parsing dates.
Step 3- Exploratory Analysis and Visualization This stage involved diving into the dataset to understand its characteristics. statistical summaries, visualizations, and descriptive analytics are utilized to identify and handle outliers, unveil patterns, trends, and potential insights. Matplotlib and Seaborn Libraries are leveraged to create impactful visualizations to effectively communicate findings. Scatterplots and barcharts are employed to find relationships between columns, and to facilitate storytelling through the visualization of data patterns.Make a note of interesting insights from the exploratory analysis.
Step 4- Asking and Answering Questions In this stage, we engage in formulating atleast four intriguing questions about the dataset. The process includes answering these questions through computations using NumPy and pandas or by creating graphical representations using Matplotlib and Seaborn. To facilitate this exploration, new columns are created, and groupings/aggregations are executed wherever necessary.
Step 5- Summarizing the analysis and drawing conclusions This section serves as a comprehensive wrap-up of the data exploration and analysis.The goal is to consolidate the findings, highlight interesting insights, and propose ideas for future work based on the dataset. Additionally, I have provided links to resources that proved useful and helpful and to enhnavce the understanding the undertaken workduring the analysis.