D O, N O T TkeDeep Learning by proximity of networking and advanced…

QuestionAnswered step-by-stepD O, N O T TkeDeep Learning by proximity of networking and advanced…D O, N O T TkeDeep Learning by proximity of networking and advanced programming WORK ON ALLALL QUESTIONS , tHEN DKINDLY DO NO T DO IF YOU DO N OT KNOW WHAT YOU ARE DOING OR I WILL REPORT Criteria Points AVOIPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3 observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5 observations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results# Import libraries for data manipulation import pandas as pd import numpy as np # Import libraries for data visualization import matplotlib.pyplot as plt import seaborn as sns from statsmodels.graphics.gofplots import ProbPlot # Import libraries for building linear regression model from statsmodels.formula.api import ols import statsmodels.api as sm from sklearn.linear_model import LinearRegression # Import library for preparing data from sklearn.model_selection import train_test_split # Import library for data preprocessing from sklearn.preprocessing import MinMaxScaler import warnings warnings.filterwarnings(“ignore”) Loading the dataIn [105]:df = pd.read_csv(“Boston.csv”) df.head() Out[105]:  CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO LSTAT MEDV0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 4.98 24.01 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 9.14 21.62 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 4.03 34.73 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 2.94 33.44 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 5.33 36.2 Observation:The price of the house indicated by the variable MEDV is the target variable and the rest of the variables are independent variables based on which we will predict the house price (MEDV).Checking the info of the dataIn [106]:df.info()  RangeIndex: 506 entries, 0 to 505 Data columns (total 13 columns): #   Column   Non-Null Count  Dtype   —  ——   ————–  —–   0   CRIM     506 non-null    float64 1   ZN       506 non-null    float64 2   INDUS    506 non-null    float64 3   CHAS     506 non-null    int64   4   NOX      506 non-null    float64 5   RM       506 non-null    float64 6   AGE      506 non-null    float64 7   DIS      506 non-null    float64 8   RAD      506 non-null    int64   9   TAX      506 non-null    int64   10  PTRATIO  506 non-null    float64 11  LSTAT    506 non-null    float64 12  MEDV     506 non-null    float64 dtypes: float64(10), int64(3) memory usage: 51.5 KBObservations:There are a total of 506 non-null observations in each of the columns. This indicates that there are no missing values in the data.There are 13 columns in the dataset and every column is of numeric data type.Exploratory Data Analysis and Data PreprocessingPart 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6 observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8ur final observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3your observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5 observations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4 observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6 observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8 final observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3bservations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5servations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6vations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3r observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5rvations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4r observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6r observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8final observations on the performance of the model on the test dataD O, N O T TkeDeep Learning by proximity of networking and advanced programming Criteria Points AVOIPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3 observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5 observations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results# Import libraries for data manipulation import pandas as pd import numpy as np # Import libraries for data visualization import matplotlib.pyplot as plt import seaborn as sns from statsmodels.graphics.gofplots import ProbPlot # Import libraries for building linear regression model from statsmodels.formula.api import ols import statsmodels.api as sm from sklearn.linear_model import LinearRegression # Import library for preparing data from sklearn.model_selection import train_test_split # Import library for data preprocessing from sklearn.preprocessing import MinMaxScaler import warnings warnings.filterwarnings(“ignore”) Loading the dataIn [105]:df = pd.read_csv(“Boston.csv”) df.head() Out[105]:  CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO LSTAT MEDV0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 4.98 24.01 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 9.14 21.62 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 4.03 34.73 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 2.94 33.44 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 5.33 36.2 Observation:The price of the house indicated by the variable MEDV is the target variable and the rest of the variables are independent variables based on which we will predict the house price (MEDV).Checking the info of the dataIn [106]:df.info()  RangeIndex: 506 entries, 0 to 505 Data columns (total 13 columns): #   Column   Non-Null Count  Dtype   —  ——   ————–  —–   0   CRIM     506 non-null    float64 1   ZN       506 non-null    float64 2   INDUS    506 non-null    float64 3   CHAS     506 non-null    int64   4   NOX      506 non-null    float64 5   RM       506 non-null    float64 6   AGE      506 non-null    float64 7   DIS      506 non-null    float64 8   RAD      506 non-null    int64   9   TAX      506 non-null    int64   10  PTRATIO  506 non-null    float64 11  LSTAT    506 non-null    float64 12  MEDV     506 non-null    float64 dtypes: float64(10), int64(3) memory usage: 51.5 KBObservations:There are a total of 506 non-null observations in each of the columns. This indicates that there are no missing values in the data.There are 13 columns in the dataset and every column is of numeric data type.Exploratory Data Analysis and Data PreprocessingPart 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6 observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8ur final observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3your observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5 observations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4 observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6 observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8 final observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3bservations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5servations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6vations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8observations on the performance of the model on the test dataDeep LearningCriteria PointsPart 1 – Question 1Normalize the train and test data2Part 1 – Question 2Build and train a ANN model as per the above mentioned architecture10Part 1 – Question 3r observations on the below plot2Part 1 – Question 4Build and train the new ANN model as per the above mentioned architecture10Part 1 – Question 5rvations on the below plot2Part 1 – Question 6Print the classification report and the confusion matrix for the test predictions.  observations on the final results4Part 2 – Question 1Complete the below code to visualize the first 10 images from the training data1Part 2 – Question 2One-hot encode the labels in the target variable y_train and y_test2Part 2 – Question 3Build and train a CNN model as per the above mentioned architecture10Part 2 – Question 4r observations on the below plot2Part 2 – Question 5Build and train the second CNN model as per the above mentioned architecture10Part 2 – Question 6r observations on the below plot2Part 2 – Question 7Make predictions on the test data using the second model1Part 2 – Question 8final observations on the performance of the model on the test dataComputer ScienceEngineering & TechnologyPython ProgrammingShare Question