WEBVTT 00:00.080 --> 00:01.040 Welcome back. 00:01.200 --> 00:09.600 Congratulations guys for this good result for displaying the actual and the predicted prices in this 00:09.600 --> 00:10.200 graph. 00:10.320 --> 00:19.240 So I want from you to try yourself all those steps and get the same results. 00:19.440 --> 00:21.720 Also congratulations guys. 00:21.720 --> 00:24.400 We well trained our model. 00:24.440 --> 00:26.040 Now let's test our model. 00:26.040 --> 00:29.840 We have a new size variable equals to Np.array. 00:29.880 --> 00:35.320 I am generating a new array here and predicted price equals to model dot predict. 00:35.320 --> 00:43.120 I am using our trained model to predict the price for a 2.5 k square feet. 00:43.280 --> 00:53.680 So we're going to test our model with how uh, how much it will cost for 2500ft² house. 00:53.880 --> 00:58.080 So we need to predict 2.5 here on the graph. 00:58.080 --> 00:59.160 2.5. 00:59.440 --> 01:02.360 It's around 9.5. 01:02.520 --> 01:12.160 So you can see here if I make the plot size 2.5 here the plotting it will predict near 9.5. 01:12.160 --> 01:19.200 So let me run this cell predicted the price for a square 2500ft². 01:19.330 --> 01:24.130 house is 9.42 K dollars. 01:24.170 --> 01:31.130 Okay, so here we are predicting the price 9.59.4. 01:31.250 --> 01:35.970 So it's similar to this equation based on the trained model. 01:36.170 --> 01:36.530 Okay. 01:36.570 --> 01:38.290 So congratulations guys. 01:38.490 --> 01:43.650 You've just built your first ML model in Python steps. 01:43.650 --> 01:45.290 We imported libraries. 01:45.290 --> 01:50.770 We create and load data visualize data split into train and test. 01:50.770 --> 01:59.970 As I said to you 80% for training and 20% for testing training the model for linear regression and evaluate 01:59.970 --> 02:00.930 performance. 02:01.130 --> 02:05.770 This is the foundation of most ML workflows. 02:05.810 --> 02:07.290 What we're going to do. 02:07.570 --> 02:12.930 You can try to use real data CSV file upload as we did here. 02:13.210 --> 02:20.610 Try other models random forest SVM and add more features, bedrooms, location and others. 02:20.650 --> 02:23.530 Okay, so you can try it with yourself. 02:23.770 --> 02:28.290 As I told you, the same steps that we've followed. 02:28.330 --> 02:30.810 Okay, so congratulations guys. 02:30.810 --> 02:36.210 We trained and well trained our first ML model.