### Machine Learning Code to Predict the Boston Housing Prices using Pandas Dataframe

In this, you will evaluate the performance and predictive of data collected from homes in the suburbs of Boston, Massachusetts.

Now we load the datasets and its related libraries:

# Import libraries necessary for this project import numpy as np import pandas as pd import visuals as vs # Supplementary code from sklearn.model_selection import ShuffleSplit # Pretty display for notebooks %matplotlib inline # Load the Boston housing dataset data = pd.read_csv('housing.csv') prices = data['MEDV'] features = data.drop('MEDV', axis = 1) # Success print('Boston housing dataset has {0} data points with {1} variables each'.format(*data.shape))

Now we will see the output of this like:

Boston housing dataset has 489 data points with 4 variables each

### Now next step is Data Exploration:

In this first section of this project, you will make a cursory investigation about the Boston housing data and provide your observations. Familiarizing yourself with the data through an explorative process is a fundamental practice to help you better understand and justify your results.

Since the main goal of this project is to construct a working model that has the capability of predicting the value of houses, we will need to separate the dataset into **features** and the **target variable**. The **features**, 'RM', 'LSTAT', and 'PTRATIO', give us quantitative information about each data point. The **target variable**, 'MEDV', will be the variable we seek to predict. These are stored in features and prices, respectively.

# FIND: Minimum price of the data minimum_price = np.min(prices) # TODO: Maximum price of the data maximum_price = np.max(prices) # TODO: Mean price of the data mean_price = np.mean(prices) # TODO: Median price of the data median_price = np.median(prices) # TODO: Standard deviation of prices of the data std_price = np.std(prices) # There are other statistics you can calculate too like quartiles first_quartile = np.percentile(prices, 25) third_quartile = np.percentile(prices, 75) inter_quartile = third_quartile - first_quartile # Show the calculated statistics print("Statistics for Boston housing dataset:\n") print("Minimum price: ${:,.2f}".format(minimum_price)) print("Maximum price: ${:,.2f}".format(maximum_price)) print("Mean price: ${:,.2f}".format(mean_price)) print("Median price ${:,.2f}".format(median_price)) print("Standard deviation of prices: ${:,.2f}".format(std_price)) print("First quartile of prices: ${:,.2f}".format(first_quartile)) print("Second quartile of prices: ${:,.2f}".format(third_quartile)) print("Interquartile (IQR) of prices: ${:,.2f}".format(inter_quartile))

**Output:**

Statistics for Boston housing dataset: Minimum price: $105,000.00 Maximum price: $1,024,800.00 Mean price: $454,342.94 Median price $438,900.00 Standard deviation of prices: $165,171.13 First quartile of prices: $350,700.00 Second quartile of prices: $518,700.00 Interquartile (IQR) of prices: $168,000.00

Now plot it:

# Using pyplot import matplotlib.pyplot as plt plt.figure(figsize=(20, 5)) # i: index for i, col in enumerate(features.columns): # 3 plots here hence 1, 3 plt.subplot(1, 3, i+1) x = data[col] y = prices plt.plot(x, y, 'o') # Create regression line plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x))) plt.title(col) plt.xlabel(col) plt.ylabel('prices')

**Output:**

### Developing a Model

# TODO: Import 'r2_score' from sklearn.metrics import r2_score def performance_metric(y_true, y_predict): # TODO: Calculate the performance score between 'y_true' and 'y_predict' score = r2_score(y_true, y_predict) # Return the score return score

Now we are calculation the performance of the model:

# Calculate the performance of this model score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3]) print("Model has a coefficient of determination, R^2, of {:.3f}.".format(score))

The model has a coefficient of determination, R^2, of 0.923.

If you need any deep learning assignment help then you can contact us at given below link references:

Get your project or assignment completed by machine learning expert and experienced developers and researchers.

**OR**

If you have project files, You can send at **codersarts@gmail.com** directly

Machine Learning Project:Predicting Boston House PricesWith Regression ...helpsto reinforce greatly the confidence in thepredictions. byGender queerThe dataset for this

projectoriginates from the UCIMachine LearningRepository. ... Statistics forBoston housingdataset: Minimumprice: $105,000.00 Maximum ...helpsto greatly reinforce the confidence in yourpredictions. byurdu poetryMachine LearningEngineer NanodegreeProjectUdacity - rahulpatraiitkgp/Predicting-Boston-Housing-Prices. ... It mayhelpyou to save some time. And I hope you don't copy any part of the code (the programming assignments are fairly easy ... byPackers and Movers in JaipurExplore and run

machine learningcode with Kaggle Notebooks | Using data from ... is a fundamental practice tohelpus better understand and justify our results. ... thisprojectis to construct a working model which has the capability ofpredicting... Statistics forBoston housingdataset: Minimumprice: $454,342.94 Maximum ... byDaily Health Updates