Skip to content

Commit 61c85b3

Browse files
ahsan-javed-dsahsan-javed-ds
committed
Adding updated the README.md file
1 parent 059033e commit 61c85b3

File tree

1 file changed

+164
-110
lines changed

1 file changed

+164
-110
lines changed

‎README.md‎

Lines changed: 164 additions & 110 deletions
Original file line numberDiff line numberDiff line change
@@ -4,7 +4,7 @@ Pakistan faces severe flooding challenges during July-August monsoon periods, wi
44

55
**Key Achievement**: The system achieves R² = 0.992 for temperature prediction and R² = 0.716 for rainfall prediction using Pakistan's average climate data, providing reliable forecasting capabilities for flood risk assessment during critical monsoon periods.
66

7-
## Project Context & Significance
7+
## -- Project Context & Significance --
88

99
### Pakistan's Flood Challenge
1010
Pakistan experiences devastating floods during July-August monsoon seasons, causing significant economic and humanitarian impacts. This predictive system addresses the urgent need for accurate climate forecasting to support:
@@ -19,7 +19,106 @@ Pakistan experiences devastating floods during July-August monsoon seasons, caus
1919
### Data Coverage
2020
The analysis utilizes comprehensive national average climate data for Pakistan, providing country-level insights while acknowledging regional variations. The 116-year historical dataset enables robust pattern recognition and long-term trend analysis essential for understanding Pakistan's complex monsoon-driven climate system.
2121

22-
## Technical Architecture
22+
---
23+
24+
## -- Production Deployment --
25+
26+
## 1. FastAPI Deployment with MLOps Pipeline
27+
28+
A stateless API with strict data validation and automated CI/CD workflows
29+
30+
### Architecture & Key Feature:
31+
32+
1. `High-Performance API`: Built with **FastAPI** for asynchronous inference and auto-generated Swagger documentation.
33+
2. `Dockerized Deployment`: Fully **containerized** environment ensuring high reproducibility across local dev and prod stages.
34+
3. `CI/CD Automation`: **GitHub Actions** pipeline performs syntax checks and dependency installation on every push.
35+
4. `Effective Error Handling`: Implements a **Fall-back strategy** to work if the models fails or input data lacks the context.
36+
5. `Type Safety`: Utilizes **pydantic** models to validate the inputs.
37+
38+
### Startup Steps:
39+
40+
**Option #1: Run with Docker (Recommended)**
41+
42+
```
43+
# Build the docker image
44+
docker build -t pakistan-climate-engine .
45+
46+
# Run the container
47+
docker run -p 8000:8000 pakistan-climate-engine
48+
```
49+
50+
**Option #2: Run with Python (Locally)**
51+
```
52+
# Install the dependencies
53+
pip install -r requirements.txt
54+
55+
# Start the server
56+
uvicorn fastapi_app:app --reload
57+
```
58+
59+
#### API Documentations:
60+
Once running, access the Swagger Ui at `http://localhost8000/docs`
61+
62+
**Endpoint: `/predict` (POST)**
63+
64+
**Request:**
65+
```
66+
{
67+
"year": 2025,
68+
"month": 7
69+
}
70+
```
71+
**Response:**
72+
```
73+
{
74+
"year": 2025,
75+
"month": "July",
76+
"rainfall": 56.6,
77+
"temperature": 28.9,
78+
"season": "Summer",
79+
"is_monsoon": true,
80+
"success": true,
81+
"method": "pipeline"
82+
}
83+
```
84+
- `method` field indicates if the prediction came from the ML Pipeline (`pipeline`), the Smart Inference function (`smart_inference`), or the Heuristic Fallback (`heuristic_fallback`)
85+
86+
---
87+
88+
## 2. Streamlit Application
89+
Interactive web application providing:
90+
- Real-time climate predictions
91+
- Historical data exploration
92+
- Flood risk assessment interface
93+
- Model performance monitoring
94+
95+
[📹 Watch Demo Video](media-assets/videos/streamlit_app.mov)
96+
97+
---
98+
99+
## 3. Flask Web Service
100+
RESTful API service offering:
101+
- Programmatic prediction endpoints
102+
- Batch processing capabilities
103+
- Model metadata access
104+
- Production-grade error handling
105+
106+
---
107+
108+
### Web Application Deployment
109+
110+
For **Streamlit** App, it's better to directly run it via Google Colab Notebook
111+
112+
For **Flask**, download the flask_app folder, create a Virtual Environmennt, install the **requirements.txt**, and run the below command:
113+
```bash
114+
python app.py
115+
```
116+
For **FastAPI**, please see the above section.
117+
118+
---
119+
120+
121+
## -- Technical Architecture --
23122

24123
### Hybrid Modeling Strategy
25124
The system implements a sophisticated dual-approach architecture:
@@ -42,7 +141,7 @@ The system employs a comprehensive 67-feature engineering pipeline with an 81.48
42141
### Conservative Feature Selection Protocol
43142
The feature selection process implements ensemble-based validation with multiple cross-validation layers to ensure production stability and prevent overfitting in the Pakistan-specific climate context.
44143

45-
## Model Development and Training
144+
## -- Model Development and Training --
46145

47146
### Three-Phase Adaptive Training Strategy
48147

@@ -53,20 +152,20 @@ The feature selection process implements ensemble-based validation with multiple
53152
This adaptive methodology demonstrates systematic model development with empirical validation at each stage.
54153

55154
### Algorithm Portfolio
56-
- Linear Regression (baseline performance benchmarking)
57-
- Ridge Regression (L2 regularization for stability)
58-
- Random Forest (ensemble robustness)
59-
- Gradient Boosting (sequential learning optimization)
60-
- XGBoost (gradient boosting with advanced regularization)
61-
- Support Vector Regression (kernel-based non-linear modeling)
155+
- `Linear Regression` (baseline performance benchmarking)
156+
- `Ridge Regression` (L2 regularization for stability)
157+
- `Random Forest` (ensemble robustness)
158+
- `Gradient Boosting` (sequential learning optimization)
159+
- `XGBoost` (gradient boosting with advanced regularization)
160+
- `Support Vector Regression` (kernel-based non-linear modeling)
62161

63162
### Production Model Performance
64163
- **Temperature Model**: R² = 0.992, demonstrating exceptional predictive accuracy
65164
- **Rainfall Model**: R² = 0.716, providing reliable forecasting for flood risk assessment
66165
- **Cross-validation**: Robust performance across multiple validation folds
67166
- **Hyperparameter Optimization**: GridSearchCV with systematic parameter tuning
68167

69-
## Flood Risk Analysis Framework
168+
## -- Flood Risk Analysis Framework --
70169

71170
### Pakistan Monsoon Pattern Analysis
72171
The system incorporates specific analysis of Pakistan's July-August flood risk periods, utilizing:
@@ -82,7 +181,7 @@ The system incorporates specific analysis of Pakistan's July-August flood risk p
82181
**Statistical Significance**: Significant but weak correlation reflecting Pakistan's complex meteorological dynamics
83182
**Interpretation**: Independent climate processes requiring specialized modeling approaches for each target variable
84183

85-
## Data Visualization and Analysis
184+
## -- Data Visualization and Analysis --
86185

87186
### Exploratory Data Analysis
88187
Comprehensive visualization suite covering:
@@ -107,24 +206,7 @@ Comprehensive visualization suite covering:
107206
- Flood risk assessment tools
108207
- Seasonal forecasting capabilities
109208

110-
111-
## Production Deployment
112-
113-
### Streamlit Application
114-
Interactive web application providing:
115-
- Real-time climate predictions
116-
- Historical data exploration
117-
- Flood risk assessment interface
118-
- Model performance monitoring
119-
120-
[📹 Watch Demo Video](media-assets/videos/streamlit_app.mov)
121-
122-
### Flask Web Service
123-
RESTful API service offering:
124-
- Programmatic prediction endpoints
125-
- Batch processing capabilities
126-
- Model metadata access
127-
- Production-grade error handling
209+
## -- Model Evaluation and Validation --
128210

129211
![Model_Evaluation](media-assets/images/flask_app.png)
130212

@@ -134,52 +216,21 @@ RESTful API service offering:
134216
- **Metadata storage**: Model performance and configuration details
135217
- **Inference functions**: Standalone prediction capabilities
136218

137-
## Installation and Setup
138-
139-
### Prerequisites
140-
```bash
141-
pip install pandas numpy scikit-learn xgboost lightgbm
142-
pip install matplotlib seaborn plotly streamlit flask joblib
143-
```
144-
145-
### Quick Start
146-
```python
147-
import joblib
148-
149-
# Load production inference function
150-
predict_climate = joblib.load('inference_function.joblib')
151-
152-
# Generate prediction for flood-critical period
153-
prediction = predict_climate(year=2024, month='august')
154-
print(f"August Rainfall Forecast: {prediction['rainfall']:.2f} mm")
155-
print(f"August Temperature Forecast: {prediction['temperature']:.2f}°C")
156-
```
157-
158-
### Web Application Deployment
159-
160-
For Streamlit App, it's better to directly run it via Google Colab Notebook
161-
162-
For Flask, download the flask_app folder, create a Virtual Environmennt, install the **requirements.txt**, and run the below command:
163-
```bash
164-
python fastapi_app.py
165-
```
166-
167-
## Model Evaluation and Validation
168-
169219
### Performance Metrics
170220
- **Root Mean Square Error (RMSE)**: Quantitative accuracy assessment
171221
- **Mean Absolute Error (MAE)**: Absolute prediction deviation
172222
- **R-squared (R²)**: Variance explanation capability
173223
- **Cross-validation scores**: Generalization performance validation
174224

175-
176225
### Validation Strategy
177226
- **Time-series cross-validation**: Temporal data integrity preservation
178227
- **Seasonal stratification**: Performance consistency across Pakistan's climate seasons
179228
- **Holdout testing**: Final model validation on unseen data
180229
- **Statistical significance testing**: Confidence interval analysis
181230

182-
## System Limitations and Considerations
231+
---
232+
233+
## -- System Limitations and Considerations --
183234

184235
### Data Scope Limitations
185236
- **Temporal Range**: Historical data limited to 1901-2016 period
@@ -196,7 +247,9 @@ python fastapi_app.py
196247
- **Data Pipeline**: Automated data quality validation protocols
197248
- **Version Control**: Model versioning and rollback capabilities
198249

199-
## Future Enhancement Roadmap
250+
---
251+
252+
## -- Future Enhancement Roadmap --
200253

201254
### Technical Improvements
202255
- **Deep Learning Integration**: LSTM networks for sequential pattern recognition
@@ -208,72 +261,73 @@ python fastapi_app.py
208261
- **Regional Models**: Province-level prediction capabilities
209262
- **Alert Systems**: Automated flood risk notifications
210263
- **Mobile Interface**: Responsive design for field applications
211-
- **API Expansion**: Enhanced programmatic access features
264+
265+
---
212266

213267
## Project Structure
214268

215269
```
216270
pakistan_temp_rainfall_predictive_modelling/
217-
├── pakistan_climate_data_analysis_and_predictive_modelling_ahsan_javed.ipynb # Main analysis notebook
218-
├── README.md # Project documentation
219-
├── DATASET_INFO.md # Dataset information and metadata
220-
├── raw_data/ # Historical climate datasets
221-
│ ├── rainfall_1901_2016_pak.csv # Pakistan rainfall data (1901-2016)
222-
│ └── tempreture_1901_2016_pakistan.csv # Pakistan temperature data (1901-2016)
223-
├── images/ # Visualization assets
224-
│ ├── Data_Visualization.png # Exploratory data analysis charts
225-
│ ├── advanced_visualization.png # Advanced statistical visualizations
226-
│ ├── climate_EDA.png # Climate trend analysis
227-
│ ├── flask_app.png # Flask application interface
228-
│ ├── flood_risk_analysis.png # Flood risk assessment charts
229-
│ └── model_evaluation.png # Model performance metrics
230-
├── videos/ # Demo videos
231-
│ └── streamlit_app.mov # Streamlit application demonstration
271+
├── .github/
272+
│ └── workflows/
273+
│ └── test.yml # CI/CD workflow test file
232274
├── Flask_app/ # Production Flask API service
233-
│ ├── app.py # Main Flask application
234-
│ ├── requirements.txt # Python dependencies
235-
│ ├── rainfall_model_pipeline.joblib # Trained rainfall model
236-
│ ├── temperature_model_pipeline.joblib # Trained temperature model
237-
│ ├── model_metadata.joblib # Model performance metadata
238-
│ ├── smart_inference_function.joblib # Optimized prediction function
239-
│ └── templates/
240-
│ └── index.html # Web interface template
241-
├── Streamlit_app/ # Interactive web dashboard
242-
│ └── streamlit_app.py # Streamlit application
243-
└── flood_risk_analysis_report/ # Analysis reports
244-
└── Pakistan_Weather_Report_20250723_0606.pdf # Comprehensive flood risk report
275+
│ ├── app.py
276+
│ ├── requirements.txt
277+
│ ├── rainfall_model_pipeline.joblib
278+
│ ├── temperature_model_pipeline.joblib
279+
│ ├── model_metadata.joblib
280+
│ ├── smart_inference_function.joblib
281+
│ └── templates/
282+
│ └── index.html # Web interface template
283+
├── Streamlit_app/
284+
│ └── streamlit_app.py # Streamlit application
285+
├── media-assets # Visualization assets
286+
│ ├── images/
287+
│ │ ├── Data_Visualization.png
288+
│ │ ├── advanced_visualization.png
289+
│ │ ├── climate_EDA.png
290+
│ │ ├── flask_app.png
291+
│ │ ├── flood_risk_analysis.png
292+
│ │ └── model_evaluation.png
293+
├── └── videos/
294+
│ └── streamlit_app.mov
295+
├── research/
296+
│ ├── dataset_info.md
297+
│ ├── flood_risk_analysis_report/ # Analysis reports
298+
│ │ └── Pakistan_Weather_Report_20250723_0606.pdf
299+
│ └── pakistan_climate_data_analysis_and_predictive_modelling_ahsan_javed.ipynb # Main analysis and training notebook
300+
├── .gitignore
301+
├── Dockerfile
302+
├── LICENSE
303+
├── README.md
304+
├── fastapi_app.py
305+
├── model_metadata.joblib
306+
├── rainfall_model_pipeline.joblib
307+
├── requirements.txt
308+
├── smart_inference_function.joblib
309+
└── temperature_model_pipeline.joblib
245310
```
246311

247-
## Research and Development Credits
248-
249-
**Main Developer**: Ahsan Javed
250-
- Machine Learning Architecture Design
251-
- Feature Engineering Framework Development
252-
- Model Training and Optimization
253-
- Production System Implementation
254-
312+
---
255313

256314
**Data Source**: CHISEL @ LUMS (Center for Climate Research and Development)
257315
[Chisel_website](https://opendata.com.pk/organization/chisel)
258316

259317
**Analysis Period**: 1901-2016 Pakistan Climate Dataset
260318
**Development Timeline**: Comprehensive iterative development with empirical validation
261319

262-
## Technical Acknowledgments
263-
264-
- **scikit-learn**: Core machine learning framework
265-
- **XGBoost/LightGBM**: Advanced gradient boosting implementations
266-
- **pandas/numpy**: Data processing and numerical computation
267-
- **matplotlib/seaborn/plotly**: Visualization and analysis tools
268-
- **Flask/Streamlit**: Web application frameworks
320+
---
269321

270322
## Contact and Collaboration
271323

272324
For technical inquiries, model improvements, or collaboration opportunities regarding Pakistan's climate prediction capabilities, please contact through the project repository or professional networks.
273325

274-
- [Linkedin](https://www.linkedin.com/in/ahsan-javed17)
275-
- [Github](https://github.com/ahsan-javed-ds)
276-
- Email: [email protected]
326+
**Author:**
327+
328+
**Ahsan Javed** _Data Scientist & ML Engineer_
329+
- **Linkedin:** [Linkedin_link](https://www.linkedin.com/in/ahsan-javed17)
330+
- **Email:** [email protected]
277331

278332
---
279333

0 commit comments

Comments
 (0)