The Objective of this assignment is to conduct exploratory data analysis (EDA) on a provided dataset. This assignment will allow you to gain hands-on experience in data preprocessing, visualization, modeling, and interpretation, essential skills for a data scientist.
- Understand the basic statistics of the dataset such as mean, median, mode, standard deviation for numerical features, such as age or BMI etc.
- Distribution of categorical variables, such as gender or diabetes etc.
- Check for missing values (e.g., "No Info") and decide how to handle them (e.g. replace or removal)
- Standardize or normalize numerical features like age or BMI etc.
- Visualize the relationships between variables using plots, scatter, histograms, or bar.
- Explore the distribution of target variable, for example, "diabetes", with respect to other features.
- Identify any patterns or anomalies in the data.
- Document the entire analysis process in a Jupyter Notebook (.ipynb file).
- Include detailed explanations of data preprocessing and exploratory data analysis.
- Prepare a concise report summarizing key findings and insights.**
- Performed EDA on a Automobile Dataset.
- Used Pandas and Numpy for Data Manipulation followed by Matplotlib and Seaborn for Data Visualisation.
- A starter project just to get acquainted with the libraries mentioned above.
Some of the visualisations and the corresponding insights:
90% of the people use cars that run of gas rather than diesel
Not a lot of people have 4 wheel drive cars
As the weight of the car increases the mileage decrease (negative correlation,colour coded by the manufacturer of the car)
Prices of BMW,Mercedes-Benz and Porsche are very high but Porsche offers car models in a lot pf price ranges.From premium pricing to pricing comparable to top models of other car manufacturers while BMW,Mercedes-Benz have only premium segment cars.
