Machine Learning Engineer Nanodegree¶

Model Evaluation & Validation¶

Project: Predicting Boston Housing Prices¶

Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

Getting Started¶

In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a good fit could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.

The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:

16 data points have an 'MEDV' value of 50.0. These data points likely contain missing or censored values and have been removed.
1 data point has an 'RM' value of 8.78. This data point can be considered an outlier and has been removed.
The features 'RM', 'LSTAT', 'PTRATIO', and 'MEDV' are essential. The remaining non-relevant features have been excluded.
The feature 'MEDV' has been multiplicatively scaled to account for 35 years of market inflation.

Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.

# Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit

# Import supplementary visualizations code visuals.py
import visuals as vs

# 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 {} data points with {} variables each.".format(*data.shape)

Boston housing dataset has 489 data points with 4 variables each.

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 which 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.

Implementation: Calculate Statistics¶

For your very first coding implementation, you will calculate descriptive statistics about the Boston housing prices. Since numpy has already been imported for you, use this library to perform the necessary calculations. These statistics will be extremely important later on to analyze various prediction results from the constructed model.

In the code cell below, you will need to implement the following:

Calculate the minimum, maximum, mean, median, and standard deviation of 'MEDV', which is stored in prices.
- Store each calculation in their respective variable.

# TODO: 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)

# 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)

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

Question 1 - Feature Observation¶

As a reminder, we are using three features from the Boston housing dataset: 'RM', 'LSTAT', and 'PTRATIO'. For each data point (neighborhood):

'RM' is the average number of rooms among homes in the neighborhood.
'LSTAT' is the percentage of homeowners in the neighborhood considered "lower class" (working poor).
'PTRATIO' is the ratio of students to teachers in primary and secondary schools in the neighborhood.

Using your intuition, for each of the three features above, do you think that an increase in the value of that feature would lead to an increase in the value of 'MEDV' or a decrease in the value of 'MEDV'? Justify your answer for each.

Hint: This problem can phrased using examples like below.

Would you expect a home that has an 'RM' value(number of rooms) of 6 be worth more or less than a home that has an 'RM' value of 7?
Would you expect a neighborhood that has an 'LSTAT' value(percent of lower class workers) of 15 have home prices be worth more or less than a neighborhood that has an 'LSTAT' value of 20?
Would you expect a neighborhood that has an 'PTRATIO' value(ratio of students to teachers) of 10 have home prices be worth more or less than a neighborhood that has an 'PTRATIO' value of 15?

Answer:

With increase in the 'RM' value usually there should be an increase in the price (MEDV). Because, when 'RM' increases the number rooms increase which eventually leads to a bigger house, or more number of rooms per person in a house. Which should be satisfactory enough for the buyers to pay more for the house.
Increase in 'LSTAT' value brings a decrease in the home price value 'MEDV' as increase in 'LSTAT' indicates more number of working poor who obviously don't prefer expensive or over priced homes most of the times. Thus, reducing the number of buyers for houses with greater prices resulting in lesser MEDV.
An increase in value of PTRATIO would decrease the value of MEDV. An increase in the student-teacher ratio means more students per teacher (PTRATIO) Hence, it is typically a result of lack of funding for the school or lack of good quality teachers wanting to work in the neighborhood - and perceived as a bad/not as good a place for families be live. Thus, people/families would rather live elsewhere and those who can afford to move do, and again, home values (MEDV) decrease as "the market" (those who can't afford to move) cannot sustain higher housing0 prices.

Developing a Model¶

In this second section of the project, you will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions.

Implementation: Define a Performance Metric¶

It is difficult to measure the quality of a given model without quantifying its performance over training and testing. This is typically done using some type of performance metric, whether it is through calculating some type of error, the goodness of fit, or some other useful measurement. For this project, you will be calculating the coefficient of determination, R², to quantify your model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how "good" that model is at making predictions.

The values for R² range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the target variable. A model with an R² of 0 is no better than a model that always predicts the mean of the target variable, whereas a model with an R² of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the features. A model can be given a negative R² as well, which indicates that the model is arbitrarily worse than one that always predicts the mean of the target variable.

For the performance_metric function in the code cell below, you will need to implement the following:

Use r2_score from sklearn.metrics to perform a performance calculation between y_true and y_predict.
Assign the performance score to the score variable.

# TODO: Import 'r2_score'
from sklearn.metrics import r2_score

def performance_metric(y_true, y_predict):
    """ Calculates and returns the performance score between 
        true and predicted values based on the metric chosen. """
    
    # TODO: Calculate the performance score between 'y_true' and 'y_predict'
    score = r2_score(y_true, y_predict)
    
    # Return the score
    return score

Question 2 - Goodness of Fit¶

Assume that a dataset contains five data points and a model made the following predictions for the target variable:

True Value	Prediction
3.0	2.5
-0.5	0.0
2.0	2.1
7.0	7.8
4.2	5.3

Run the code cell below to use the performance_metric function and calculate this model's coefficient of determination.

# 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)

Model has a coefficient of determination, R^2, of 0.923.

Would you consider this model to have successfully captured the variation of the target variable?
Why or why not?

Hint: The R2 score is the proportion of the variance in the dependent variable that is predictable from the independent variable. In other words:

R2 score of 0 means that the dependent variable cannot be predicted from the independent variable.
R2 score of 1 means the dependent variable can be predicted from the independent variable.
R2 score between 0 and 1 indicates the extent to which the dependent variable is predictable. An
R2 score of 0.40 means that 40 percent of the variance in Y is predictable from X.

Answer:

In this case, based on the model's R² score of 0.923, I would say that the model successfully captured 92.3 % of variation in the target variables.

Implementation: Shuffle and Split Data¶

Your next implementation requires that you take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset.

For the code cell below, you will need to implement the following:

Use train_test_split from sklearn.cross_validation to shuffle and split the features and prices data into training and testing sets.
- Split the data into 80% training and 20% testing.
- Set the random_state for train_test_split to a value of your choice. This ensures results are consistent.
Assign the train and testing splits to X_train, X_test, y_train, and y_test.

# TODO: Import 'train_test_split'
from sklearn.cross_validation import train_test_split

# TODO: Shuffle and split the data into training and testing subsets
X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.20, random_state=23)

# Success
print "Training and testing split was successful."

Training and testing split was successful.

Question 3 - Training and Testing¶

What is the benefit to splitting a dataset into some ratio of training and testing subsets for a learning algorithm?

Hint: Think about how overfitting or underfitting is contingent upon how splits on data is done.

Answer: The most significant benefit of splitting our dataset into training and testing data is validation, i.e being able to validate/measure the accuracy of our training / model.

If the entire dataset is used for training, it might lead to overfitting of data to model. That is, the dataset would not be generalized, instead, the model will already know the answers to the patterns we have in our training data. And like-wise, if we use the entire dataset for testing, the model would not have learned anything and would be over generalized - guessing at the answers with very little accuracy.

Splitting the dataset into a suitable ratio of training and testing optimizes the scenario of using just enough data to train the algorithm for a good fit, leaving unseen data (by the model), for us to validate/measure the accuracy of the model and avoid errors due to high bias or high variance.

Analyzing Model Performance¶

In this third section of the project, you'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, you'll investigate one particular algorithm with an increasing 'max_depth' parameter on the full training set to observe how model complexity affects performance. Graphing your model's performance based on varying criteria can be beneficial in the analysis process, such as visualizing behavior that may not have been apparent from the results alone.

Learning Curves¶

The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R², the coefficient of determination.

Run the code cell below and use these graphs to answer the following question.

# Produce learning curves for varying training set sizes and maximum depths
vs.ModelLearning(features, prices)

Question 4 - Learning the Data¶

Choose one of the graphs above and state the maximum depth for the model.
What happens to the score of the training curve as more training points are added? What about the testing curve?
Would having more training points benefit the model?

Hint: Are the learning curves converging to particular scores? Generally speaking, the more data you have, the better. But if your training and testing curves are converging with a score above your benchmark threshold, would this be necessary? Think about the pros and cons of adding more training points based on if the training and testing curves are converging.

Answer:

I choose the graph with max_depth = 3
The more the training points are added, its score decreases and tends to level off, while the variance / uncertainty of the curve are also decreasing. The testing curve's score also increases as more data points are provided up until approximately 300, as it then tends to level off and run parallel with the training curve.
Provided that both training and validation curves have levelled off, providing more training points wouldn't benefit the model with significant improvements but may only increase time consumption for training and testing.

Complexity Curves¶

The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the learning curves, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the performance_metric function.

Run the code cell below and use this graph to answer the following two questions Q5 and Q6.

vs.ModelComplexity(X_train, y_train)

Question 5 - Bias-Variance Tradeoff¶

When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance?
How about when the model is trained with a maximum depth of 10? What visual cues in the graph justify your conclusions?

Hint: High bias is a sign of underfitting(model is not complex enough to pick up the nuances in the data) and high variance is a sign of overfitting(model is by-hearting the data and cannot generalize well). Think about which model(depth 1 or 10) aligns with which part of the tradeoff.

Answer:

With a maximumn depth of 1, according to the complexity curve both the training and validation scores are low, the model suffers from high bias (underfitting).
At a maximum depth of 10, the model has high variance (overfitting) according to the curve above.
Typically, a model has high bias when a minimal number of features are used. This gives a low R² value (near 0) and leading to underfitting (i.e. it is over generalized) with very low accuracy scores both in training and testing.
While a model that has high variance, is the exact opposite. It has a high R,² value (approaching 1) and it is overfitting to the data (i.e. it is not generalized enough) with very high scores on the training data but low score on testing data (i.e. data it hasn't seen before).

Question 6 - Best-Guess Optimal Model¶

Which maximum depth do you think results in a model that best generalizes to unseen data?
What intuition lead you to this answer?

Hint: Look at the graph above Question 5 and see where the validation scores lie for the various depths that have been assigned to the model. Does it get better with increased depth? At what point do we get our best validation score without overcomplicating our model? And remember, Occams Razor states "Among competing hypotheses, the one with the fewest assumptions should be selected."

Answer:

A max_depth of 3 should do the job of achieving the best generalized model. As, at max_depth of 3, both validation and training curves are at their smallest level of uncertainty between each other, while the validation score is near its highest value.

Whereas, at a max_depth of 4, 5 and above both training and validation curves start to diverge, where we can see the training curve approaching a score of 1, indicating that it's perfectly matching the data points leading to overfitting (high variance) and almost same validation score for max depths 4, 5 and for others the validation score curve trends downward.

Evaluating Model Performance¶

In this final section of the project, you will construct a model and make a prediction on the client's feature set using an optimized model from fit_model.

Question 7 - Grid Search¶

What is the grid search technique?
How it can be applied to optimize a learning algorithm?

Hint: When explaining the Grid Search technique, be sure to touch upon why it is used, what the 'grid' entails and what the end goal of this method is. To solidify your answer, you can also give an example of a parameter in a model that can be optimized using this approach.

Answer:

The grid search technique automates the process of tuning parameters (especially, hyperparameters) of a model in order to get the best performance. Hyperparameters are simply the knobs and levels we pull and turn when building a machine learning classifier. The process of tuning hyperparameters is more formally called hyperparameter optimization.
The Grid Search techinque will methodically (and exhaustively) train and evaluate a machine learning classifier for each and every combination of hyperparameter values. The point is to identify which hyperparameters are likely to work best.

Question 8 - Cross-Validation¶

What is the k-fold cross-validation training technique?
What benefit does this technique provide for grid search when optimizing a model?

Hint: When explaining the k-fold cross validation technique, be sure to touch upon what 'k' is, how the dataset is split into different parts for training and testing and the number of times it is run based on the 'k' value.

When thinking about how k-fold cross validation helps grid search, think about the main drawbacks of grid search which are hinged upon using a particular subset of data for training or testing and how k-fold cv could help alleviate that. You can refer to the docs for your answer.

Answer:

The k-fold cross-validation training technique is the process of dividing the data points into smaller number of k bins. The model is tested on one of the k bins and trained on the other k-1 bins. This process of testing and training, occurs k times across all bins for testing and training. The average of the k testing experiments is used as the overall result of the model.

Even if grid search automates the parameter selection and tuning for best performance, not using cross-validation could result in the model being tuned only to a specific subset of data.

Because, without using a technique such as cross-validation for example, only using k-fold to create testing and training data, will not shuffle the data points in the dataset (i.e if the dataset is ordered or in any particular pattern. Then, grid search would only perform tuning on the same subset of training data. Utilizing cross-validation, eliminates the above issue by using the entire dataset allowing grid search to optimize parameter tuning across all data points.

Implementation: Fitting a Model¶

Your final implementation requires that you bring everything together and train a model using the decision tree algorithm. To ensure that you are producing an optimized model, you will train the model using the grid search technique to optimize the 'max_depth' parameter for the decision tree. The 'max_depth' parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction. Decision trees are part of a class of algorithms called supervised learning algorithms.

In addition, you will find your implementation is using ShuffleSplit() for an alternative form of cross-validation (see the 'cv_sets' variable). While it is not the K-Fold cross-validation technique you describe in Question 8, this type of cross-validation technique is just as useful!. The ShuffleSplit() implementation below will create 10 ('n_splits') shuffled sets, and for each shuffle, 20% ('test_size') of the data will be used as the validation set. While you're working on your implementation, think about the contrasts and similarities it has to the K-fold cross-validation technique.

Please note that ShuffleSplit has different parameters in scikit-learn versions 0.17 and 0.18. For the fit_model function in the code cell below, you will need to implement the following:

Use DecisionTreeRegressor from sklearn.tree to create a decision tree regressor object.
- Assign this object to the 'regressor' variable.
Create a dictionary for 'max_depth' with the values from 1 to 10, and assign this to the 'params' variable.
Use make_scorer from sklearn.metrics to create a scoring function object.
- Pass the performance_metric function as a parameter to the object.
- Assign this scoring function to the 'scoring_fnc' variable.
Use GridSearchCV from sklearn.grid_search to create a grid search object.
- Pass the variables 'regressor', 'params', 'scoring_fnc', and 'cv_sets' as parameters to the object.
- Assign the GridSearchCV object to the 'grid' variable.

# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import make_scorer
from sklearn.grid_search import GridSearchCV

def fit_model(X, y):
    """ Performs grid search over the 'max_depth' parameter for a 
        decision tree regressor trained on the input data [X, y]. """
    
    # Create cross-validation sets from the training data
    # sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)
    # sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)
    cv_sets = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.20, random_state = 0)

    # TODO: Create a decision tree regressor object
    regressor = DecisionTreeRegressor()

    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10
    params = {'max_depth':range(1,11)}

    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' 
    scoring_fnc = make_scorer(performance_metric)

    # TODO: Create the grid search cv object --> GridSearchCV()
    # Make sure to include the right parameters in the object:
    # (estimator, param_grid, scoring, cv) which have values 'regressor', 'params', 'scoring_fnc', and 'cv_sets' respectively.
    grid = GridSearchCV(regressor, param_grid=params, scoring=scoring_fnc, cv=cv_sets)

    # Fit the grid search object to the data to compute the optimal model
    grid = grid.fit(X, y)

    # Return the optimal model after fitting the data
    return grid.best_estimator_

Making Predictions¶

Once a model has been trained on a given set of data, it can now be used to make predictions on new sets of input data. In the case of a decision tree regressor, the model has learned what the best questions to ask about the input data are, and can respond with a prediction for the target variable. You can use these predictions to gain information about data where the value of the target variable is unknown — such as data the model was not trained on.

Question 9 - Optimal Model¶

What maximum depth does the optimal model have? How does this result compare to your guess in Question 6?

Run the code block below to fit the decision tree regressor to the training data and produce an optimal model.

# Fit the training data to the model using grid search
reg = fit_model(X_train, y_train)

# Produce the value for 'max_depth'
print "Parameter 'max_depth' is {} for the optimal model.".format(reg.get_params()['max_depth'])

Parameter 'max_depth' is 5 for the optimal model.

Answer:

The model has a max depth of 5.
This isn't a surprising result compared to my guess. Because, I have primarily considered the validation score (approximately 0.8) in my answer to question 6 and then considered the amount by which the two curves diverged and to be precise and flawless I chose the max depth that has minimum divergence with the same validation score (i.e. max_depth 3). But, it turns out that a little divergence between training and validation score curves is acceptable.

Question 10 - Predicting Selling Prices¶

Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:

Feature	Client 1	Client 2	Client 3
Total number of rooms in home	5 rooms	4 rooms	8 rooms
Neighborhood poverty level (as %)	17%	32%	3%
Student-teacher ratio of nearby schools	15-to-1	22-to-1	12-to-1

What price would you recommend each client sell his/her home at?
Do these prices seem reasonable given the values for the respective features?

Hint: Use the statistics you calculated in the Data Exploration section to help justify your response. Of the three clients, client 3 has has the biggest house, in the best public school neighborhood with the lowest poverty level; while client 2 has the smallest house, in a neighborhood with a relatively high poverty rate and not the best public schools.

Run the code block below to have your optimized model make predictions for each client's home.

# Produce a matrix for client data
client_data = [[5, 17, 15], # Client 1
               [4, 32, 22], # Client 2
               [8, 3, 12]]  # Client 3

# Show predictions
for i, price in enumerate(reg.predict(client_data)):
    print "Predicted selling price for Client {}'s home: ${:,.2f}".format(i+1, price)

Predicted selling price for Client 1's home: $421,095.65
Predicted selling price for Client 2's home: $230,522.73
Predicted selling price for Client 3's home: $964,162.50

Answer:

Client 1 --> 421,095.65 Dollars: It seems reasonable due to the just below average poverty level and a not bad student-to-teacher ratio.And, at 409k this home is within one std of the mean (i.e. approximately 454K) with 5 rooms, which is a pretty good price to buy.

Client 2 --> 230,522.73 Dollars: - The minimum selling price in the area is just over 100K. However, the 4 rooms in the house help raise the price of their home, given that both teacher-student ratio and provery levels are quite high both of which negatively impact the selling price.

Client 3 --> 964,162.50 Dollars: - With high number of rooms, very low neighborhood poverty levels and a low student-teacher ratio, though this home's selling price is near the maximum selling price in the neighborhood. It is reasonably priced considering the above features.

Sensitivity¶

An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted.

Run the code cell below to run the fit_model function ten times with different training and testing sets to see how the prediction for a specific client changes with respect to the data it's trained on.

vs.PredictTrials(features, prices, fit_model, client_data)

Trial 1: $391,183.33
Trial 2: $419,700.00
Trial 3: $415,800.00
Trial 4: $420,622.22
Trial 5: $413,334.78
Trial 6: $411,931.58
Trial 7: $399,663.16
Trial 8: $407,232.00
Trial 9: $351,577.61
Trial 10: $413,700.00

Range in prices: $69,044.61

Question 11 - Applicability¶

In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.

Hint: Take a look at the range in prices as calculated in the code snippet above. Some questions to answering:

How relevant today is data that was collected from 1978? How important is inflation?
Are the features present in the data sufficient to describe a home? Do you think factors like quality of apppliances in the home, square feet of the plot area, presence of pool or not etc should factor in?
Is the model robust enough to make consistent predictions?
Would data collected in an urban city like Boston be applicable in a rural city?
Is it fair to judge the price of an individual home based on the characteristics of the entire neighborhood?

Answer:

The constructed model can't be used in a real-world setting. As, are a number of reasons for this, I've highlighted a few below:

Information Relvance: The data which the current model has been trained on, is collected in 1978, is not relevant to today (2016)
Applicability: A model training on data from a city such as Boston, is not suitable to be used in urban areas such as Ohio nor would be it applicable for other cities such as San Francisco.
Features: Although the dataset covers features which are present in today's homes, it is missing features that could affect the selling price in today's housing market such as size of a backyard, approximity to public transit if the home is in a large city, and now a days with home automation and security coming into picture.
Robust Nature: The current model appears to be too sensitive/not well generalized as running it multiple times for a specific client (as seen above) provides a wide variance in pricing, which is unsatifactory in the real-world.

Supplying the model with more data, between 1978 and 2016, along with using a few additional features, the model may be robust and accurate enough to be applied to data from cities similar to Boston in the real-world.