Weather analysis #571

Weather Analysis/Dataset/README.md

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+# Weather Analysis Dataset
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, Weather Analysis and Prediction.( https://www.kaggle.com/datasets/mastmustu/weather-analysis)
+
+## About the dataset
+
+The data contains day wise weather attributes from 2009 to July 2020. Our CSV file has 22 columns and 3902 entries(Rows).
+
+**Columns Description**:
+
+- Date
+- Average temperature (°F)
+- Average humidity (%)
+- Average dewpoint (°F)
+
+- Average barometer (in)
+
+- Average windspeed (mph)
+
+- Average gustspeed (mph)
+
+- Average direction (°deg)
+
+- Rainfall for month (in)
+
+- Rainfall for year (in)
+
+- Maximum rain per minute
+
+- Maximum temperature (°F)
+
+- Minimum temperature (°F)
+
+- Maximum humidity (%)
+
+- Minimum humidity (%)
+
+- Maximum pressure
+
+- Minimum pressure
+
+- Maximum windspeed (mph)
+
+- Maximum gust speed (mph)
+
+- Maximum heat index (°F)
+
+- Month

Weather Analysis/README.md

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+<h1>Weather Analysis</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the Average Rainfall per month for a given atmospheric conditions like temperature, humidity , dewpoint, pressure, windspeed, etc.
+
+**DATASET**
+
+[https://www.kaggle.com/datasets/mastmustu/weather-analysis]
+
+**DESCRIPTION**
+
+To analyze the dataset of Weather Analysis and build and train the model on the basis of different features and variables.
+
+The datasets have a csv file with 3902 entries, 22 columns.
+
+**Columns Description**:
+
+- Date
+- Average temperature (°F)
+- Average humidity (%)
+- Average dewpoint (°F)
+- Average barometer (in)
+- Average windspeed (mph)
+- Average gustspeed (mph)
+- Average direction (°deg)
+- Rainfall for month (in)
+- Rainfall for year (in)
+- Maximum rain per minute
+- Maximum temperature (°F)
+- Minimum temperature (°F)
+- Maximum humidity (%)
+- Minimum humidity (%)
+- Maximum pressure
+- Minimum pressure
+- Maximum windspeed (mph)
+- Maximum gust speed (mph)
+- Maximum heat index (°F)
+- Month
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="Distribution" src="./Images/distribution plot 1.png">
+
+<img alt="Distribution" src="./Images/distribution plot 2.png">
+
+<img alt="Regression" src="./Images/avg barometer vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/avg dewpoint vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/avg humidity vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/avg temp vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/avg windspeed vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/max temp vs rainfall per mnth.png">
+
+<img alt="Regression" src="./Images/month vs rainfall per month.png">
+
+
+**MODELS USED**
+
+| Model                     | MSE_train | R2_train | MSE_test  | R2_test   |
+|---------------------------|-----------|----------|-----------|-----------|
+|Random Forest Regression	  | 0.0126    |	0.965291 | 0.082938	 | 0.773470  |
+|XGBoost Regression	        | 0.0056    |	0.984504 | 0.089369	 | 0.755905  |
+|Decision Tree	            | 0.58e-34  | 1.000000 | 0.144070	 | 0.606500  |
+|Riddge Regression	        | 3.58e-34	| 1.000000 | 0.144070  | 0.606500  |
+|Linear Regression	        | 0.274    	| 0.243614 | 0.281541  | 0.231021  |
+|Elastic Net Regression	    | 2.94e-01	| 0.190594 | 0.302724	 | 0.173166  |
+|Neural Network Regression	| 0.358     | 0.076272 | 0.405645	 |-0.107945  |
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which is CSV format.
+* It has 3902 entries(Rows), 22 columns.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. XGBoost
+6. Tensorflow
+7. Keras
+8. Sci-py
+9. Seaborn
+
+
+
+**CONCLUSION**
+
+- Random Forest and XGBoost Regression models show promising performance with lower MSE and higher R-square values for both training set and dataset.
+- Decision Tree Regression achieved perfect R-square on the training set but on the test set it's value is 0.6, indicating overfitting.
+- Deep Neural Network (NN) has a high MSE and negative R-square on testing set, approximately zero on training set, suggesting poor performance on both training and test sets.
+
+
+**YOUR NAME**
+
+*Ghousiya Begum*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ghousiya-begum-a9b634258/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ghousiya47)

Weather Analysis/requirements.txt

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+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2
+tensorflow==2.4.1
+keras==2.4.0