Відео

can u plz send whatsapp

Thank you 🙏

Could u please make a video on other ai models like rbf, anfis Or rnn?

Excellent very simple way explained. Thank you for the KT. Can u plz share the dataset and code...I ll practice at my end once.

where i can download the dataset???

Thanks. Can you make a video on Bayesian optimization/Random forest optimization technique for DNN(deep neural network)

App or website name please

Can you share the notebook with us? I need it so bad

Here is the full working code of this video, Use jupyter notebook for understanding. import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import math dataset = pd.read_csv("50_Startups.csv") print(dataset.shape) dataset.head() plt.scatter(dataset['Marketing Spend'] , dataset['Profit']) plt.title('Multiple Regression') plt.xlabel('Marketing Spend') plt.ylabel('Profit') plt.show() plt.scatter(dataset['Administration'] , dataset['Profit']) plt.title('Multiple Regression') plt.xlabel('Administration') plt.ylabel('Profit') plt.show() plt.scatter(dataset['R&D Spend'] , dataset['Profit']) plt.title('Multiple Regression') plt.xlabel('R & D Spend') plt.ylabel('Profit') plt.show() dataset['newyork']=np.where(dataset['State'] == 'New York' , 1, 0) dataset['florida']=np.where(dataset['State'] == 'Florida' , 1, 0) dataset['California']=np.where(dataset['State'] == 'California' , 1, 0) dataset['profit'] = dataset['Profit'] dataset.drop(columns=['State'], axis=1,inplace=True) dataset.drop(columns=['Profit'], axis=1,inplace=True) print(dataset.head()) X=dataset.iloc[:,:6] y=dataset.iloc[:,6:] X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size = 0.25) from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train =scaler.fit_transform(X_train) X_test = scaler.fit_transform(X_test) X_train[0:5] model = LinearRegression() model.fit(X_train , y_train) ypred = model.predict(X_test) print(ypred) math.sqrt(mean_squared_error(y_test, ypred)) r2_score(y_test, ypred)

Thanks a ton. Very nice explanation. I am 66 years old new entrant to this field. But my results are showing different. 1) your code no.23 - math.sqrt(mean_squared_error(y_test, y_pred)) - Result is 9137.990152794946 where as mine is 15023.010725678312. 2)your code no.24 - r2_score(y_test, y_pred) - 0.9347068473282425 whereas mine shows 0.8235262062096441. Please tell me am I making any mistake? Sorry for bothering you.

thank you so much sir. It is very clear explanation and helpful.

Sir I am getting key error in creating the figure object can u please help me with this

Good explanation. I don't know if you did not check for outliers on purpose.

Thanks that's really helpful

thank you so much very clear explanation very helpful.

Some error come how solve

Sir how to save our model

Awesome Video ...very helpful.

can i plot regression line on this?

Thank you. What is the purpose of scaling the exogenous variables before regression? It’s my understanding that that will only change the scale of the betas but not the significance of them.

Can you provide the link to the notebook used in the video?

Awesome video and great Algo. Can you please share the notebook with us?

✌️✌️

Edit : At 0:36, the count method gives the number of times an element occurs in a list. The author told by mistake that it gives the index which is not correct.