sachinezhilmaran Goto Github PK

1)a) Data Cleaning:

import pandas as pd df=pd.read_csv("Data_set.csv") print(df.head()) print(df.tail()) print(df.info()) print(df.dtypes) print(df.isnull().sum()) df.dropna(inplace='True') x = df['height'].mean() df['height'].fillna(x, inplace=True) df['Date'] = pd.to_datetime(df['Date']) df.drop_duplicates(inplace = True)

1)b) Detect and Remove Outliers:

import pandas as pd import numpy as np df=pd.read_csv("Data_set.csv") print(df.head()) print(df.tail()) print(df.info()) print(df.dtypes) print(df.isnull().sum()) import matplotlib.pyplot as plt plt.boxplot(data['price']) plt.show() Q1 = data['price'].quantile(0.25) Q3 = data['price'].quantile(0.75) IQR = Q3 - Q1 lower = Q1 - 1.5IQR upper = Q3 + 1.5IQR upper_array = np.where(data['price']>=upper)[0] lower_array = np.where(data['price']<=lower)[0] data.drop(index=upper_array, inplace=True) data.drop(index=lower_array, inplace=True) plt.boxplot(data['price']) plt.show()

2,3) Feature Selection Techniques:

import pandas as pd from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 data = pd.read_csv("CarPrice.csv") print(df.head()) print(df.tail()) print(df.info()) print(df.dtypes) print(df.isnull().sum()) df.dropna(inplace='True') df.drop_duplicates(inplace = True) data["fueltype"]=data["fueltype"].map({"gas":1,"diesel":0}) data.drop(['enginetype','carbody','symboling','CarName','aspiration','doornumber','drivewheel','enginelocation','cylindernumber','fuelsystem'], axis=1, inplace=True) selector = SelectKBest(chi2, k=10) data = selector.fit_transform(data, data["horsepower"]) #Enter the column that is of int64 datatype not float64 print("Selected Features:",data.shape)

4,5) Data Visualization:

import pandas as pd data = pd.read_csv("CarPrice.csv") print(df.head()) print(df.tail()) print(df.info()) print(df.dtypes) print(df.isnull().sum()) df.dropna(inplace='True') df.drop_duplicates(inplace = True) import matplotlib.pyplot as plt import seaborn as sns sns.boxplot(data['horsepower']) plt.show() sns.countplot(data['horsepower']) plt.show() sns.histplot(data['horsepower']) plt.show() sns.lineplot(data['horsepower']) plt.show() x = data['fueltype'].value_counts() plt.pie(x.values, labels=x.index, autopct='%1.1f%%') plt.show() sns.barplot(x=data['fueltype'],y=data['horsepower']) plt.show() sns.scatterplot(data['horsepower']) plt.show() sns.heatmap(data['horsepower'].corr(), annot=True) plt.show()