Skip to content

Latest commit

 

History

History
116 lines (76 loc) · 6.06 KB

README.md

File metadata and controls

116 lines (76 loc) · 6.06 KB
Raw

Rain In Australia Prediction

rain in australia

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. replace dataset

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

missing data visualization

Univariate Analysis Between Numerical Features

univariate analysis

Outlier Visualization

bloxplot vis

Bivariate Analysis

bivariate analysis