<jupyter_start><jupyter_text>Objective - 1: Import the dataset, explore for dimensionality, and type and average value of the horsepower across all the cars. Also, identify a few of mostly correlated features, which would help in modification<jupyter_code>#import libraries
import numpy as np
import pandas as pd
df = pd.read_csv('C:/data/SalaryGender.csv',delimiter = ',')
Salary = np.array(df['Salary'])
Gender = np.array(df['Gender'])
Age = np.array(df['Age'])
print(df)
#Dimensionality Check
df.shape
#Type of Dataset
type(df)
#Check the unique value
df['Age'].unique()
#Feature Mean
df.mean()
#Feature Median
df.median()
#Feature Mode
df.mode()
#Plotting a Heatmap with Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
correlations = df.corr()
sns.heatmap(data = correlations,square = True, cmap = "viridis")
            
plt.yticks(rotation=0)
plt.xticks(rotation=90)<jupyter_output><empty_output><jupyter_text>Objective - 2: Check for missing values and outliers within the horsepower column and remove them.<jupyter_code>#Missing Value Detection
df.isna().any()
#Dealing with Outlier for Salary
import seaborn as sns
sns.boxplot(x=df['Salary'])
filter = df['Salary']<150
df_out_rem = df[filter]
sns.boxplot(x=df_out_rem['Salary'])
#Dealing with Outlier for Age
import seaborn as sns
sns.boxplot(x=df['Age'])<jupyter_output><empty_output>
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