MUMBAI MONTHLY RAINFALL CAPSTONE PROJECT

### Data Description and Analysis Summary for Presentation

#### 1. **Importing Libraries**
Libraries used:
- `pandas`, `numpy`: Data manipulation
- `matplotlib`, `seaborn`: Data visualization
- `scikit-learn`: Machine learning utilities
- `statsmodels`, `pmdarima`: Statistical modeling
- `keras`: Deep learning models

#### 2. **Loading and Exploring the Dataset**
**Dataset Overview:**
- **Source:** CSV file (`mumbai-monthly-rains.csv`)
- **Columns:**
- `Year`: The year of the recorded data.
- `Jan` to `Dec`: Monthly rainfall data.
- `Total`: Total annual rainfall.

**Initial Data Checks:**
- Displayed first few rows.
- Summary statistics (mean, standard deviation, min, max).
- Checked for missing values.
- Verified data types.

**Visualizations:**
- **Annual Rainfall Time Series:** Trends in annual rainfall over the years.
- **Monthly Rainfall Over Years:** Patterns and variations in monthly rainfall.
- **Yearly Total Rainfall Distribution:** Distribution and frequency of annual rainfall.
- **Box Plots for Monthly Data:** Spread and outliers in monthly rainfall.
- **Correlation Matrix of Monthly Rainfall:** Relationships between different months' rainfall.

#### 3. **Data Transformation**
**Steps:**
- Ensured 'Year' column is of integer type.
- Created a datetime index.
- Converted monthly data to a time series format.
- Created lag features to capture past values.
- Generated rolling statistics (mean, standard deviation) for different window sizes.
- Added seasonal indicators (dummy variables for months).
- Dropped rows with NaN values.

**Result:**
- Transformed dataset with additional features ready for time series analysis.

#### 4. **Data Splitting**
**Procedure:**
- Split the data into features (`X`) and target (`y`).
- Further split into training (80%) and testing (20%) sets without shuffling to preserve time series order.

**Result:**
- Training set: `(X_train, y_train)`
- Testing set: `(X_test, y_test)`

#### 5. **Automated Hyperparameter Tuning**
**Tool Used:** `pmdarima`
- Automatically selected the best parameters for the SARIMA model.
- Evaluated using metrics such as AIC and BIC.

**Output:**
- Best SARIMA model parameters and statistical summary.

#### 6. **SARIMA Model**
**Steps:**
- Fit the SARIMA model using the training data.
- Evaluated on both training and testing sets using MAE and RMSE.

**Output:**
- **Train MAE:** Indicates accuracy on training data.
- **Test MAE:** Indicates accuracy on unseen data.
- **Train RMSE:** Measures average error magnitude on training data.
- **Test RMSE:** Measures average error magnitude on testing data.

#### 7. **LSTM Model**
**Preparation:**
- Reshaped data for LSTM input.
- Converted data to `float32`.

**Model Building and Training:**
- Built an LSTM model with one LSTM layer and one Dense layer.
- Trained the model on the training data.

**Evaluation:**
- Evaluated on both training and testing sets using MAE and RMSE.

**Output:**
- **Train MAE:** Accuracy on training data.
- **T

1. Name of Capstone Project

2. Agenda

3. Title Click to edit Master text styles Second level Third level Fourth level Fifth level

4. Title C ontent

5. Questions ?

6. Thank You!