Metadata-Version: 2.4
Name: cl1codes
Version: 0.2.0
Summary: A Python package to quickly access Computer Laboratory - I (BE AI&DS) practical codes, theory, experiment mappings, and automated datasets
Author: Ayyub
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: requests
Requires-Dist: scipy
Dynamic: license-file

# cl1codes 🚀

A Python package to quickly access **Computer Laboratory - I (BE AI&DS)** practical source codes, theoretical background, experiment aim mappings, and automatically extract associated datasets.

---

## 📦 Installation

Once published to PyPI, you can install the package on any machine using:

```bash
pip install cl1codes
```

To install locally in editable mode (development):

```bash
pip install -e .
```

---

## ⚡ Quickstart

```python
import cl1codes as cl

# 1. Print all experiments, aims, and dataset mappings
cl.print_mapping()
# or
cl.list_experiments()

# 2. Print source code for Practical 1 (automatically extracts Wine.csv)
cl.print_practical(1)

# 3. Print code for sub-experiments (e.g. 3A, 3B, 5A, 5B)
cl.print_practical('3A')
cl.print_practical('3B')

# 4. Print theory for an experiment
cl.print_theory(4)

# 5. Extract a dataset manually into the current working directory
cl.download_dataset(8)

# 6. Extract all 10 bundled datasets at once
cl.download_all_datasets()
```

---

## 📋 Available Practicals & Aim Mappings

| Exp # | Experiment Title | Aim & Focus | Datasets |
|---|---|---|---|
| **1** | PCA Algorithm | Dimensionality reduction on wine variables | `Wine.csv` |
| **2** | Descriptive Statistics & Regression | Univariate, Bivariate, and Multiple Regression on Diabetes | `diabetes.csv` |
| **3A** | Support Vector Machines (SVM) | Classifying 8x8 handwritten digits into classes 0-9 | *Built-in (sklearn digits)* |
| **3B** | K-Nearest Neighbours (KNN) | Social Network Ads purchase prediction & full confusion matrix | `Social_Network_Ads.csv` |
| **4** | K-Means Clustering | Iris flower partitioning & optimal K selection via Elbow Method | `iris.csv` |
| **5A** | Random Forest Classifier | Predicting car safety and acceptability | `car_evaluation.csv` |
| **5B** | Ensemble Learning | Comparative voting, AdaBoost, GBM, and XGBoost on Iris | `iris.csv` |
| **6A** | Reinforcement Learning - Maze | Q-Learning agent exploring a grid maze environment | *None (Synthetic/Grid World)* |
| **6B** | Reinforcement Learning - Tic-Tac-Toe | Q-Learning agent with Bellman equation & epsilon-greedy policy | *None (Synthetic/RL Game)* |
| **7** | Interacting with Web APIs | OpenWeatherMap REST API weather retrieval & visualization | *None (REST API)* |
| **8** | Data Cleaning and Preparation | Telecommunications customer churn cleaning & imputation | `Telecom_Customer_Churn.csv` |
| **9** | Data Wrangling on Real Estate | Merging with demographics, one-hot encoding, and outlier management | `RealEstate_Prices.csv`, `Neighborhood_Demographics.csv` |
| **10** | Data Visualization with Matplotlib | City Air Quality Index (AQI) trends & multi-pollutant plots | `City_Air_Quality.csv` |
| **11** | Data Aggregation | Sales performance analysis by region & product category | `Retail_Sales_Data.csv` |

---

## 🌟 Key Features

- **Instant Source Code Access**: View clean, ready-to-run source code for every practical.
- **Experiment Aim Mapping**: Use `cl.print_mapping()` anytime in Python or Jupyter Notebooks to instantly know what aim belongs to what experiment.
- **Offline & Bundled Datasets**: All 10 practical datasets (~1 MB) are bundled directly inside the package. When running a practical, the required dataset is automatically copied to your current working directory without needing external internet access.
- **Detailed Theory & Formulas**: Deep-dive into PCA mathematics, Bellman equation, SVM kernels, and statistical measures using `cl.print_theory(n)`.

---

## 📄 License

MIT License.
