Rain In Australia Prediction
In this we have to predict whether there will Rain tomorrow or not. This is a classification problem.
Dataset contain 10 years of daily weather observation from different location across Autralia. There are 23 columns and 145460 records.
How to Run
Running on Kaggle
- Fork my Kaggle Kernel to run it. To fork the kernel, you have to click on Copy and Edit Button on Top Right Corner.
How to run locally
- Download the Jupyter notebook from my github. I have also attached the jupyter notebook as a part of submission.
- Install Required Dependencies (Pandas, missingno, matploblob, seaborn, numpy, sklearn).
- Download the dataset.
- Replace the dataset path.
I would recomend using Kaggle Kernel to run this project, because dataset contains lot of row, and it has lot of visualization.
Feature Engineering or Data Preprocessing
- For Numberical Feature: We have used Deterministic linear Regression to impute numerical features.
- For Categorical Feature: We have used mode per location to impute missing value of that location. In case of Location with no categorical value, we have used the mode of complete column.
Feature Selection
- We have used Lasso Regression and Pearson Correlation to select relevant features.
- Any independent feature with Zero Correlation with Dependent feature (RainTomorrow) is dropped.
- Any independent feature with more than 0.9 value with other independent feature is dropped.
Selected Features
['Rainfall', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm','Humidity3pm', 'Pressure3pm', 'Cloud3pm', 'WindDir9am_ENE','WindDir9am_N', 'WindDir9am_NE', 'WindDir9am_NNE', 'WindDir9am_S','WindDir9am_SSW', 'WindDir3pm_N', 'WindDir3pm_NNE', 'WindDir3pm_NNW', 'WindDir3pm_NW', 'WindDir3pm_SSE', 'WindDir3pm_WNW', 'RainToday']
Model Selection and Cross Validation
- We have utilized stratifed K fold cross validation technique to compute the accuracy of model.
- stratifed K fold cross validation is used because of imbalance dataset and to ensure that train dataset contain equal number of records from each group.
- We are performing 10 experiments in Stratified K Fold Cross validation.
Utlized Model
Logistic Regression
- List of possible accuracy:
[0.8432558779045786, 0.8407809707135983, 0.841880929465145, 0.839543517118108, 0.8421559191530318, 0.8404372336037399, 0.8424309088409184, 0.8443558366561253, 0.8397497593840231, 0.8462807644713323] - Maximum Accuracy That can be obtained from this model is: 84.62 %
- Minimum Accuracy: 83.95 %
- Overall Accuracy: 84.20 %
- Standard Deviation is: 0.20 %
K Nearest Neighbours
- List of possible accuracy:
[0.8269627388972913, 0.824487831706311, 0.8278564553829232, 0.8222879142032173, 0.8249003162381411, 0.8279939502268665, 0.8299188780420734, 0.8324625326550255, 0.8264815069434897, 0.8278564553829232] - Maximum Accuracy That can be obtained from this model is: 83.24 %
- Minimum Accuracy: 82.22 %
- Overall Accuracy: 82.71 %
- Standard Deviation is: 0.27 %
Descision Tree
- List of possible accuracy:
[0.7709335899903753, 0.7775333424996562, 0.7762958889041661, 0.7737522342912141, 0.7706586003024887, 0.7735459920252991, 0.7769146157019112, 0.7717585590540355, 0.778220816719373, 0.7782895641413446] - Maximum Accuracy That can be obtained from this model is: 77.82 %
- Minimum Accuracy: 77.06 %
- Overall Accuracy: 77.47 %
- Standard Deviation is: 0.28 %
XGBoost
- List of possible accuracy:
[0.8422246665750034, 0.8429808882166919, 0.8394060222741647, 0.8394747696961364, 0.8420184243090885, 0.8392685274302214, 0.8444933315000688, 0.841468444933315, 0.8404372336037399, 0.8437371098583804] - Maximum Accuracy That can be obtained from this model is: 84.44 %
- Minimum Accuracy: 83.92 %
- Overall Accuracy: 84.15 %
- Standard Deviation is: 0.17 %
Naive Bayes Classifier.
- List of possible accuracy:
[0.8101883679362024, 0.807300976213392, 0.8081259452770521, 0.8036573628488932, 0.8081259452770521, 0.8077822081671937, 0.8118383060635226, 0.8107383473119758, 0.8129382648150695, 0.8117008112195793] - Maximum Accuracy That can be obtained from this model is: 81.29 %
- Minimum Accuracy: 80.36 %
- Overall Accuracy: 80.92 %
- Standard Deviation is: 0.26 %
| Metrics | Logistics Regression | K Nearest Neighbours | Decision Tree | XGBoost | Naïve Bayes Classifier | Random Forest Classifier |
|---|---|---|---|---|---|---|
| Maximum Accuracy | 84.62% | 83.24% | 77.82% | 84.44% | 81.29% | 85.03% |
| Minimum Accuracy | 83.95% | 82.22% | 77.06% | 83.92% | 80.36% | 84.29% |
| Overall Accuracy | 84.20% | 82.71% | 77.47% | 84.15% | 80.92% | 84.69% |
| Standard Deviation | 0.20% | 0.27% | 0.28% | 0.17% | 0.26% | 0.21% |
Best Models: Logistic Regssion and XGBoost with accuracy of 84% approx.
Visualizations
Missing Data Visualization
Univariate Analysis Between Numerical Features
Outlier Visualization
Bivariate Analysis
