In this assignment, you will work with a provided dataset containing about $10$ years of daily weather

observations from many locations across Australia. Your goal is to perform an extensive data cleaning

and preprocessing process followed by feature engineering. This assignment will give you hands$-$on

experience in preparing data for analysis and modeling, as well as enhancing the predictive power of

features.

Dataset:

This dataset contains about $10$ years of daily weather observations from numerous Australian weather

stations.

Task Overview: $($Whichever applies to your problem$)$

Phase $1$: Data Preprocessing and Cleaning:

$1.$ Handling Missing Values:

$$ Identify columns with missing values.

$$ Decide on an appropriate strategy for handling missing values $($imputation$,$ removal, etc.$).$

$$ Apply the chosen strategy to clean the dataset.

$2.$ Handling Outliers:

$$ Identify columns with potential outliers.

$$ Choose a method to detect and handle outliers $($z$-$score, IQR, domain knowledge, etc.$).$

$$ Implement the outlier handling technique.

$3.$ Dealing with Duplicate Records:

$$ Identify and handle duplicate records if present in the dataset.

$4.$ Data Transformation:

$$ Convert categorical variables into numerical representations $($one$-$hot and label encoding, etc.$).$

$$ Scale or normalize numerical features if needed.

Phase $2$: Feature Engineering:

$5.$ Creating New Features:

$$ Analyze the existing features and brainstorm new features that might be valuable for prediction.

$$ Examples: Feature interactions, ratios, derived statistics, etc.

$6.$ Dimensionality Reduction:

$$ Consider dimensionality reduction techniques $($PCA$,$ feature selection$)$ if the dataset has a high

number of features.

$7.$ Binning and Discretization:

$$ Implement binning or discretization for continuous features to convert them into categorical

ones.

$8.$ Feature Scaling:

$$ Implement feature scaling methods such as Min$-$Max Scaling or Standardization.