Step $1$: Understand the data

The first step of exploratory data analysis is to explore the number of instances, number of features, and type of each feature. A data frame contains feature names and descriptions for all features in a dataset. Data frames are presented as tables or bulleted lists and are important resources for understanding a dataset.$$

A$-1.(5$ points$)$ Read the HW$03\_$Dataset into pandas DataFrame df$\_$main, and create two other data frames by selecting data from the df$\_$main, one has only data of categorical features, and the second contains data of numerical features call them df$\_$cat, and df$\_$num, respectively.$$

a$.$ How many instances are in the HW$03\_$Dataset?

b$.$ How many features are in the HW$03\_$Dataset in total?

c$.$ Which columns are categorical in the dataset?

Step $2$: Identify relationships between features

Once the dataset's size and features are understood, the relationships between features should be explored. Data visualizations like scatter plots give a visual representation of how pairs of features change together. Scatter plots and other visualizations may suggest possible models for making predictions or describing relationships. Ex: If two features have a strong linear relationship, then a model based on a straight line might be a good fit.

A$-2.(10$ points$)($a$)$ Let us suppose that our target variable is the price, an important step towards understanding the data features is to identify their relationship with the target variable. Depict a pair plot that shall help show the relation of features against the target variable and differentiate that with the brand$\_$name. $($b$)$ Determine and print the strength and direction of the relationship of each numerical feature against the price column.$$

$$A$-3.(6$ points$)$ Studying the memory RAM may reveal more information. Our project sponsor is keen to find answers to the following questions:

a$.$ Which brand produces phones with all possible RAM sizes?

b$.$ Which brands produce phones with $12$ GB of RAM?$$

c$.$ Which brands produce phones with $1\mathrm{.}5$ GB and $512$ MB of RAM?$$

$$A$-4.(6$ points$)$ EDA involves understanding the distribution of the data as well as determining if we have to deal with outliers. Based on the prices, we may find brands with outliers. $$

a$.$ Use a box plot to visualize the price column differentiated with brands

b$.$ How many Redmi phones would be considered outliers based on the price column? Select and print the instances that are Redmi and outliers.$$

A$-5.(10$ points$)$Given the mobile dataset, we noticed several numerical and categorical features. Using Principal Component Analysis $($PCA$),$ we can reduce the dimensionality of the dataset and yet retrain a reasonable amount of information. We assume price column is our target variable, using Python code to perform PCA and answer the following:

a$.$ How many principal components $($total$)$ that we can get by applying the PCA on the mobile dataset that assuming price is a target variable? $($hint: don$$t convert categorical columns into numerical$)$

b$.$ How much variations were captured by the first two components, round your results into $4$ decimal places?$$

c$.$ Using PCA which of the numerical features contribute the most to the first principal component? Sort the features based on their contribution to the first principal component.

$6\mathrm{.}4$ HW$03-$ Topic $6$ Coding Tasks

The mobile dataset is a collection of information about various mobile Android phones. It includes a range of features, as tabulated below; the features are relevant to a consumer's decision$-$making process when purchasing a new phone.

The objective of this exercise is to analyze the relationship between various phone specifications and their corresponding ratings and reviews. Conduct the following tasks:

LAB