https://github.com/whereishussain/data-science
Projects related Data Visualisation, Cleaning, Preprocessing, Machine Learning, Deep Learning, ANN and CNN Projects and Model Training and Model Evaluation
https://github.com/whereishussain/data-science
data-cleaning-and-preprocessing data-science data-visualisation machine-learning machine-learning-models model-training-and-evaluation neural-networks
Last synced: 4 months ago
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Projects related Data Visualisation, Cleaning, Preprocessing, Machine Learning, Deep Learning, ANN and CNN Projects and Model Training and Model Evaluation
- Host: GitHub
- URL: https://github.com/whereishussain/data-science
- Owner: WhereisHussain
- Created: 2025-04-12T14:40:23.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-05-09T22:16:44.000Z (5 months ago)
- Last Synced: 2025-06-03T12:15:52.302Z (4 months ago)
- Topics: data-cleaning-and-preprocessing, data-science, data-visualisation, machine-learning, machine-learning-models, model-training-and-evaluation, neural-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 451 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧠 Data Science Projects Repository
This repository contains a collection of practical data science projects covering the complete machine learning workflow, including:
- 🧹 Data Cleaning
- 🧪 Data Preprocessing
- 📊 Data Visualization
- 🏗️ Model Building & Compilation
- 🏃 Model Training & Evaluation
These projects aim to help learners and practitioners understand each phase of working with data and machine learning models.
---
## 📁 Project Structure
### 1. 🧼 Data Cleaning Project
**Objective:**
Clean and standardize a raw dataset with missing values, duplicates, incorrect data types, and inconsistent formatting.
**Techniques Used:**
- Handling missing data (mean, median, drop)
- Removing duplicates
- Converting data types
- String formatting and trimming
- Date and time conversion
**Tools:** `pandas`, `numpy`
📄 File: `data_cleaning.py`
---
### 2. ⚙️ Data Preprocessing Project
**Objective:**
Prepare clean data for machine learning algorithms by transforming features and labels.
**Techniques Used:**
- Feature scaling (StandardScaler, MinMaxScaler)
- Encoding categorical variables (OneHotEncoder, LabelEncoder)
- Train-test split
- Data balancing (optional: SMOTE)
**Tools:** `pandas`, `scikit-learn`, `numpy`
---
### 3. 📈 Data Visualization Project
**Objective:**
Understand the dataset using visual exploration techniques and identify patterns or anomalies.
**Techniques Used:**
- Histograms, box plots, scatter plots
- Correlation heatmaps
- Pair plots
- Class distribution graphs
**Tools:** `matplotlib`, `seaborn`, `pandas`
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### 4. 🧠 Model Compilation & Training Project
**Objective:**
Build a machine learning or deep learning model, compile it with appropriate configurations, and train it on prepared data.
**Steps Covered:**
- Defining a model (ML or DL)
- Choosing loss function, optimizer, metrics
- Model training with validation
- Accuracy and loss plots
**Tools:** `scikit-learn`, `keras` / `tensorflow`, `matplotlib`
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### 5. 🔍 Evaluation & Testing
**Objective:**
Evaluate model performance using appropriate metrics and visualize the results.
**Evaluation Metrics:**
- Accuracy, precision, recall, F1-score
- Confusion matrix
- ROC-AUC (for classification)
---