A collection of SPPU Artificial Intelligence & Data Science (AI & DS) practicals for Data Modeling & Visualization.
This repository contains simple, beginner-friendly Python and Jupyter Notebook implementations of the practicals, using libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, and Statsmodels.
📚 Created for learning, practical work, revision, and viva preparation.
Practical Topic Main Concepts
Lab 1 Data Loading, Storage CSV, Excel, JSON, and File Formats Pandas, Data Cleaning
Lab 3 Data Cleaning and Missing Values, Preparation Duplicates, Data Types, Feature Engineering
Lab 4 Data Wrangling Filtering, Merging, Encoding, Aggregation, Outliers
Lab 5 Data Visualization Line Plot, Bar Plot, using Matplotlib Pie Chart, Box Plot, Scatter Plot
Lab 6 Data Aggregation GroupBy, Pivot Table, Sales Analysis, Bar Chart, Pie Chart
Covers loading and working with CSV, Excel, and JSON files, data cleaning, basic analysis, and visualization.
File: Lab_1_Data_Loading_Storage_and_File_Formats_Simple.ipynb
Covers basic data cleaning techniques such as handling missing values, removing duplicates, correcting data types, feature engineering, and preparing data for analysis.
File: Lab_3_Data_Cleaning_and_Preparation.ipynb
Covers filtering, merging, handling categorical variables, aggregation, and basic outlier handling.
File: Lab_4_Data_Wrangling.ipynb
Covers different visualization techniques including:
- Line plots
- Bar charts
- Pie charts
- Box plots
- Scatter plots
- Labels, titles, and legends
File: Lab_5_Data_Visualization_using_Matplotlib.ipynb
Covers grouping and summarizing data using Pandas.
Topics include:
groupby()pivot_table()- Sales by region
- Sales by product category
- Bar charts
- Pie charts
- Grouped bar charts
File: Lab_6_Data_Aggregation.ipynb
Covers basic time-series analysis and visualization.
Topics include:
- Date and time conversion
- Stock-price trends
- Moving averages
- Seasonality
- Correlation
- Trading volume
- Exponential smoothing
- Basic forecasting
File: Lab_7_Time_Series_Data_Analysis.ipynb
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
- Statsmodels
The practicals use datasets available through Scikit-learn
(sklearn.datasets) wherever possible.
For practicals where Scikit-learn does not provide the required real-world dataset, a simple example dataset is created using an sklearn dataset.
This makes the notebooks:
- Easy to run
- Easy to understand
- Reproducible
- Suitable for practical demonstrations
git clone https://github.com/parthraorane86/SPPU-AIDS-Data-Modeling-Visualization-Practicals.gitcd SPPU-AIDS-Data-Modeling-Visualization-Practicalsjupyter notebookOpen any .ipynb file and run the cells.
You can also open the notebooks using Google Colab.
Install the required libraries with:
pip install pandas numpy matplotlib scikit-learn statsmodels openpyxlUniversity: Savitribai Phule Pune University (SPPU)
Branch: Artificial Intelligence & Data Science (AI & DS)
Subject: Data Modeling & Visualization / Computer Laboratory
The main purpose of this repository is to provide simple and understandable practical implementations for SPPU AI & DS students.
It can be useful for:
- 📖 Practical learning
- 💻 Coding practice
- 📝 Lab submissions
- 🎤 Viva preparation
- 🔍 Understanding data analysis concepts
- 👨🎓 Academic reference
If this repository helped you with your practicals or learning, consider giving it a ⭐ Star.
If you notice an error or have a better implementation, feel free to open an Issue or submit a Pull Request.
This repository is intended for educational and learning purposes.
Practical requirements, datasets, and implementations may differ between colleges or academic years. Always compare the notebooks with the practical questions provided by your college or SPPU.
Parth Raorane
Artificial Intelligence & Data Science
SPPU
SPPU · SPPU AIDS · SPPU AI DS · SPPU Practicals ·
AIDS Practicals · Data Modeling · Data Visualization ·
Python Practicals · Jupyter Notebook · SPPU Computer Laboratory