Program 1 Admission Dataset # Admission Chance Predictor Based on Test Scores and Profile # This project uses a Logistic Regression model to predict # whether a student will be admitted (1) or not...
Program 1 Admission Dataset # Admission Chance Predictor Based on Test Scores and Profile # This project uses a Logistic Regression model to predict # whether a student will be admitted (1) or not...
Program 1 College Dataset # To build a machine learning model that can predict the annual tuition fee of a # private college based on: # Its ranking , The student satisfaction score ,...
Program 1 Retail Sales Dataset # Project Sales Forecasting for a Retail Store # To predict future sales for a retail store based on historical sales data # using a Linear Regression model. import...
Program 1 College Dataset # To build a machine learning model that can predict the annual tuition fee of a # private college based on: # Its ranking , The student satisfaction score ,...
Program 1 Covid Dataset #COVID-19 Cases Analysis Using Seaborn # Project shows informative line chart and HeatMap chart # showing the growth of confirmed COVID-19 cases over time for Italy, US, India, and Brazil....
Program 1 E-Commerce Sales Dataset # E-Commerce Sales Visualization using Seaborn #This project analyzes e-commerce sales data using Seaborn and Matplotlib. # It visualizes trends over time, category-wise performance, regional patterns, # and customer...
Program 1 Iris Dataset import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn.ensemble import RandomForestClassifier df=pd.read_csv(“D://scikit_data/IrisData/Iris.csv”) df.head() df.shape df.info() df.isnull().sum() df=df.drop(‘Id’,axis=’columns’)...
Program 1 Seaborn Dataset import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) df_tips.head() df_tips.info() df_tips.isnull().sum() df_tips.shape sns.pairplot(df_tips,hue=’sex’,palette=’Accent_r’) sns.pairplot(df_tips,hue=’sex’,kind=’scatter’) sns.pairplot(df_tips,hue=’sex’,markers=’*’) sns.pairplot(df_tips,hue=’sex’)
Program 1 Seaborn Dataset import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt df_sales=pd.read_excel(“D://scikit_data/Sale/sales.xlsx”) df_sales.head() df_sales.info() df_sales.isnull().sum() df_sales.shape sns.relplot(x=’Sales’,y=’Profit’,data=df_sales,hue=’Order Priority’,style=’Ship Mode’) sns.relplot(x=’Sales’,y=’Profit’,data=df_sales,size=’Discount’) sns.relplot(x=’Sales’,y=’Profit’,data=df_sales,size=’Discount’,sizes=(20,200),hue=’Order Priority’) df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) df_tips.head() df_tips.info()...
