1.

INTRODUCTION TO PYTHON
1.1 PYTHON

Python is a widely used, high-level, general-purpose programming language known

for its simplicity and readability. It enables developers to express concepts with fewer lines
of code compared to languages like C++ or Java. Python’s clean syntax and design
philosophy make it accessible to beginners while providing powerful tools for experienced
developers to work on both small and large-scale applications.

Python supports multiple programming paradigms, including object-oriented,

procedural, imperative, and functional programming styles. Additionally, Python features a
dynamic type system, automatic memory management, and an extensive standard library,
making it suitable for a wide range of applications, from web development to automation.

1.2 APPLICATIONS OF PYTHON

Python’s versatility allows it to be used in a wide variety of fields and applications,
including:
 Web Development: Frameworks like Django and Flask make it easy to build web
applications with Python.
 Machine Learning: Python is widely used for building and deploying machine learning
models, which simplify tasks such as regression, classification, and clustering in various
domains like finance, healthcare, and marketing.
 Data Science: Apart from machine learning, Python is heavily used for data analysis,
statistical modeling, and data visualization.
 Artificial Intelligence: Beyond ML, Python is also used in broader AI fields such as
natural language processing (NLP), computer vision, and reinforcement learning.

1.3 PYTHON IN MACHINE LEARNING

Python is particularly popular in the field of machine learning (ML) due to its
simplicity, community support, and the availability of powerful libraries and frameworks.
Python has several libraries and frameworks that make machine learning easier, such as:
 NumPy and Pandas for efficient data manipulation and analysis.
 Scikit-learn for classical machine learning algorithms (like regression, classification,
clustering).
 TensorFlow and PyTorch for deep learning and neural networks.

1.4 DATATYPES IN PYTHON

The primary data types in Python are:

 Integer int: Represents whole numbers, both positive and negative, without a fractional
component. e.g: 10, -5.
 Float float: Represents numbers that contain decimal points or fractions. e.g: 3.14, -
0.75.
 String str: Represents a sequence of characters enclosed in single, –double, or triple
quotes. e.g: "Python", 'Hello World'.
 Boolean bool: Represents logical values, either True or False.
 List list: An ordered, mutable collection of items, which can be of different data types.
e.g: [1, "apple", 3.5].
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  Tuple tuple: An ordered, immutable collection of items. e.g: (1, 2, "banana").
 Set set: An unordered collection of unique elements. e.g: {1, 2, 3, "apple"}.
 Dictionary dict: A collection of key-value pairs, where each key is associated with a
specific value. e.g: {"name": "Alice", "age": 25}.

1.5 CONDITIONAL STATEMENTS IN PYTHON

1. if Statement: Executes a block of code if a condition is true.

Ex:
x = 10
if x > 5:
print("x is greater than 5")

2. else Statement: Executes a block of code if the condition in the if statement is false.
Ex:
x=3
if x > 5:
print("x is greater than 5")
else:
print("x is not greater than 5")

3. elif Statement: Checks another condition if the previous if condition is false.

Ex:
x=5
if x > 5:
print("x is greater than 5")
elif x == 5:
print("x is equal to 5")

4. Nested if Statement: An if statement within another if statement.

Ex:
x=10
if x>5:
if x<15:
print(“Between 5 and 15”)

1.6 ITERATIVE STATEMENTS IN PYTHON

1. for Loop: Iterates over a sequence (such as a list, tuple, or string) and executes a block of
code for each item in the sequence.
Ex:
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
print(fruit)

2. while Loop: Repeatedly executes a block of code as long as a specified condition is True.
Ex:
x=0

2
 while x < 5:
print(x)
x += 1

3. break Statement: Exits the loop prematurely when a certain condition is met.
Ex:
for i in range(5):
if i == 3:
break
print(i)

4. continue Statement: Skips the current iteration and moves to the next iteration in the loop.
Ex:
for i in range(5):
if i == 2:
continue
print(i)

1.7 FUNCTIONS IN PYTHON

Functions are reusable blocks of code that perform a specific task. They allow for
code modularization, making programs easier to read, maintain, and debug.

A function in Python is defined using the def keyword, followed by the function
name and parentheses containing optional parameters.

Syntax:
def function_name(parameters):

# Function body
# Optional return statement

Ex:

def add(a, b):

"""Returns the sum of two numbers."""

return a + b

result = add(5, 3) # Result: 8

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 2. OOPS IN PYTHON

Python is an object-oriented programming (OOP) language that allows for the creation and
utilization of classes and objects. The major principles of object-oriented programming in
Python include the following:

1. Object:
An object is an entity that has a state (attributes) and behavior (methods). Objects can be
physical (e.g., a mouse, keyboard) or logical (e.g., a user account). In Python, everything is
an object, and all objects have associated attributes and methods. For instance, functions
have a built-in attribute __doc__, which returns the docstring defined in the function source
code.
2. Class:
A class is a blueprint for creating objects. It defines a set of attributes and methods that
the created objects (instances) will have. For example, an Employee class may contain
attributes such as email, name, age, and salary.
Syntax:

class ClassName:

# Class attributes and methods

attribute = value

def method_name(self):

# Method body

3. Method:
A method is a function that is associated with an object. In Python, methods are not
unique to class instances; any object type can have methods.

4. Inheritance:
Inheritance is a feature of OOP that allows one class (the derived class or child class) to
inherit the properties and behaviors of another class (the base class or parent class). This
promotes code reusability and reduces redundancy.
Example:

class Parent:

def greet(self):
print("Hello from Parent")

class Child(Parent):

def greet_child(self):
print("Hello from Child")

5. Polymorphism:
Polymorphism allows methods to perform different functions based on the object that is
calling them. It enables a single interface to be used for different underlying forms (data
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 types). For instance, a method named talk can have different implementations for different
animal classes.
Example:

class Animal:

def talk(self):
pass

class Dog(Animal):

def talk(self):
return "Bark"

class Cat(Animal):

def talk(self):
return "Meow"

6. Encapsulation:
Encapsulation is a mechanism that restricts access to certain components of an object
and bundles the data (attributes) and methods (functions) that operate on the data within a
single unit. This can be implemented using private variables and getter/setter methods to
control access.
Example:

class Account:

def __init__(self, balance):

self.__balance = balance # Private attribute

def get_balance(self):
return self.__balance

def deposit(self, amount):

self.__balance += amount
7. Data Abstraction:
Data abstraction refers to the concept of hiding the complex reality while exposing only
the necessary parts. It allows for the implementation of complex systems while providing a
simpler interface to the user.

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 3. NUMPY & PANDAS
3.1 NUMPY
NumPy is a powerful library for numerical computing in Python, providing support for
large, multi-dimensional arrays and matrices, along with a collection of mathematical functions
to operate on these arrays.

3.1.1 ARRAY
An array is a central data structure in NumPy, allowing for efficient storage and
manipulation of numerical data. It is a grid of values, all of the same type, indexed by a tuple of
non-negative integers. The number of dimensions is called the rank of the array.

3.1.2 PYTHON LIST vs NUMPY ARRAY

NumPy gives you an enormous range of fast and efficient numerically-related options.
While a Python list can contain different data types within a single list, all of the elements in a
NumPy array should be homogenous. The mathematical operations that are meant to be
performed on arrays wouldn't be possible if the arrays weren't homogenous.

Why NumPy?
NumPy arrays are faster and more compact than Python lists. An array consumes less
memory and is far more convenient to use. NumPy uses much less memory to store data and
it provides a mechanism of specifying the data types, which allows the code to be optimized
even further.

3.1.3 CREATING AND ACCESSING 1D ARRAY

To create an array in NumPy, first, you need to import the library.

import numpy as np

# Creating a 1D array
array_1d = np.array([1, 2, 3, 4, 5])

Adding Elements to an Array:

NumPy arrays have a fixed size, so you cannot add elements directly. However, you can use
np.append() to create a new array.

# Adding elements to an array

new_array = np.append(array_1d, [6, 7])

Removing Elements from an Array:

You can use np.delete() to remove elements from an array.

# Removing elements from an array

modified_array = np.delete(array_1d, 2) # Removes the element at index 2
Sorting an Array:
NumPy provides a built-in method to sort arrays.

# Sorting an array
sorted_array = np.sort(array_1d)

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Resizing an Array:
You can change the shape of an array using the reshape() method.

# Resizing an array
reshaped_array = np.reshape(array_1d, (5, 1)) # Reshaping to 5 rows and 1 column

Indexing:
You can access elements of a NumPy array using indexing.

# Indexing an array
element = array_1d[2] # Accesses the element at index 2

Slicing:
Slicing allows you to access a range of elements in an array.

# Slicing an array
slice_array = array_1d[1:4] # Gets elements from index 1 to 3

Broadcasting:
Broadcasting refers to the ability of NumPy to perform operations on arrays of different shapes.
For instance, you can add a scalar to an array.

# Broadcasting example
array_broadcast = array_1d + 5 # Adds 5 to each element in the array

3.1.4 2D ARRAYS (MATRIX)

To create a 2D array (matrix), you can pass a list of lists to the np.array() function.

# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Accessing Elements in a 2D Array:

Elements in a 2D array can be accessed using row and column indices.
The syntax is array[row_index, column_index].

# Accessing an element
element = array_2d[1, 2] # Accesses the element in the second row and third column
print("Element at (1, 2):", element) # Output: 6

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3.2 PANDAS
Pandas is a powerful Python library used for data manipulation and analysis,
especially in structured data environments. It provides versatile data structures to efficiently
work with large datasets.

3.2.1 IMPORTING PANDAS

To use Pandas, you need to import the library:

import pandas as pd

3.2.2 DATASTRUCTRES IN PYTHON

1. Series: A one-dimensional labeled array, similar to a list, where each element has an
associated index.
2. DataFrame: A two-dimensional, tabular data structure with labeled axes (rows and
columns), similar to a table in relational databases.
3. Panel: A three-dimensional container for data, though less commonly used and largely
replaced by multi-dimensional arrays in `NumPy`.

3.2.3 DATAFRAME
A `DataFrame` is the primary data structure in Pandas, used to store and manipulate
tabular data.

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}

df = pd.DataFrame(data)
print(df)

3.2.4 IMPORTING DATA FROM CSV FILE

To load data from a CSV file into a Pandas DataFrame:

df = pd.read_csv('file.csv')
print(df.head()) # Displays the first 5 rows

3.2.5 SORTING AND SLICING RECORDS

Pandas allows sorting and slicing of records for data manipulation.

# Sorting by a column
sorted_df = df.sort_values(by='Age')

# Slicing specific rows and columns

subset_df = df.loc[0:2, ['Name', 'Age']] # Select rows 0 to 2 and columns 'Name' and 'Age'

3.2.6 MERGING TWO DATAFRAMES

To merge two DataFrames based on a common column:

df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})

df2 = pd.DataFrame({'ID': [1, 2], 'Score': [85, 90]})
merged_df = pd.merge(df1, df2, on='ID')

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3.2.7 GROUPING AND CONCATENATION
Pandas allows grouping data for aggregation and concatenating DataFrames.

# Grouping by a column and applying an aggregate function

grouped_df = df.groupby('Age').mean()

# Concatenating two DataFrames

concatenated_df = pd.concat([df1, df2], axis=1) # Concatenate along columns

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 4. INTRODUCTION TO MACHINE LEARNING
Machine Learning (ML) is a branch of artificial intelligence (AI) that creates algorithms that
learn from data and make predictions or decisions. Rather than being manually programmed for
specific tasks, ML models find patterns in data and improve automatically with experience. This
ability makes machine learning useful for many applications, including data analysis and
automating decisions.

4.1 MACHINE LEARNING WORKFLOW

1. Data Collection
The first step involves gathering and preparing data from different sources. The quality and
quantity of data significantly influence model performance.

2. Data Preprocessing
The data is cleaned and transformed to prepare it for the model. This includes handling missing
values, normalizing features, and encoding categorical variables.

3. Model Building
A machine learning algorithm is selected and trained on the prepared data to learn the underlying
patterns.

4. Model Evaluation
The model's performance is assessed using metrics like accuracy, precision, recall, and mean
squared error, often with a validation dataset.

5. Model Deployment
Once the model is trained and evaluated, it is deployed for real-time predictions or integrated into
applications for decision-making.

4.2 APPLICATIONS OF MACHINE LEARNING

Machine learning has a wide array of applications across different industries:

Healthcare: Predicting diseases, medical image analysis, personalized treatment recommendations.

Finance: Fraud detection, stock market prediction, risk assessment, credit scoring.
Marketing: Customer segmentation, recommendation engines, targeted advertising.
Autonomous Vehicles: Self-driving cars use machine learning to perceive the environment and
make driving decisions.

4.3 MACHINE LEARNING CATEGORIES

Machine learning techniques are broadly categorized into the following types:
1. Supervised_Learning
In supervised learning, the algorithm learns from labeled training data to make predictions.
Each training example includes both the input data and the corresponding output label.
Example Algorithms: Linear Regression, Decision Trees, Support Vector Machines (SVM).
2. Unsupervised_Learning
In unsupervised learning, the algorithm is given data without explicit labels and must find
structure within the data. This is often used in exploratory data analysis.

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 Example Algorithms: K-Means Clustering, Hierarchical Clustering, Principal Component
Analysis (PCA).
3. Semi-Supervised_Learning
Semi-supervised learning is a hybrid approach where the model is trained on a small amount
of labeled data along with a large amount of unlabeled data. This technique helps when
labeling data is costly or time-consuming.
4. Reinforcement_Learning
In reinforcement learning, the model learns through trial and error by interacting with an
environment and receiving feedback in the form of rewards or penalties.

4.4 INTRODUCTION TO LINEAR REGRESSION

Linear regression is a statistical technique used to model the relationship between one or
more independent variables and a dependent variable by fitting a linear equation to observed data.
It is widely used in predictive analytics, machine learning, and data science to understand and
predict continuous outcomes. The primary goal of linear regression is to find the best-fitting line
that minimizes the difference between predicted and actual values.

Fig 1: Linear Regression

4.4.1 SIMPLE LINEAR REGRESSION

Simple linear regression is the most basic form of linear regression where we model the
relationship between a single independent variable (X) and a dependent variable (Y). The
relationship between the two is represented by the equation:

Y = β0 + β 1 X + ϵ
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Where:

 Y is the dependent variable (outcome),

 X is the independent variable (predictor),
 β0is the intercept (value of Y when X=0),
 β1 is the slope (change in Y for a unit change in X),
 ϵ is the error term (the difference between observed and predicted values).

Simple linear regression is useful when there is only one predictor variable, and the relationship
between the variables is assumed to be linear.

4.4.2 MULTIPLE LINEAR REGRESSION

Multiple linear regression extends simple linear regression by incorporating more than one
independent variable to predict the dependent variable. The equation for multiple linear regression
is:

Y = β0 + β1 X1+ β2 X2 +….+ βn Xn + ϵ

Where:

 X1 , X2 , . . . , Xn are the independent variables (predictors),

 β0 , β1 , . . . , βn are the coefficients (representing the influence of each predictor on Y).

Multiple linear regression helps in modeling more complex relationships between the
dependent variable and multiple predictors, providing more accurate predictions than simple linear
regression when multiple factors are involved.

4.4.3 MODEL EVALUATION MATRICES

To evaluate the performance of a linear regression model, several metrics are commonly used:

1. Mean Squared Error (MSE): The average squared difference between the observed and
predicted values. Lower values indicate better model performance.
i=1
1
MSE= ∑ ¿¿
n n

2. Root Mean Squared Error (RMSE): The square root of MSE, providing an interpretable
error metric in the same units as the dependent variable.

RMSE=√ MSE

3. R-squared (R²): Represents the proportion of variance in the dependent variable that is
explained by the independent variables. Values range from 0 to 1, with higher values
indicating a better fit

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 4. Adjusted R-squared: Similar to R², but adjusted for the number of predictors in the model,
preventing overfitting when using multiple independent variables.

Each of these metrics provides insights into how well the linear regression model fits the data and
how accurately it predicts outcomes.

4.5 INTRODUCTION TO LOGISTIC REGRESSION

Logistic regression is a statistical method used to model the relationship between one or
more independent variables and a categorical dependent variable. Unlike linear regression, logistic
regression is used when the dependent variable is binary or categorical. It estimates the probability
that a given input belongs to a certain category by fitting the data to a logistic curve. The output of
logistic regression is a probability between 0 and 1, which can then be used to classify observations.

4.5.1 BINARY LOGISTIC REGRESSION

Binary logistic regression is the simplest form of logistic regression where the dependent
variable has two possible outcomes (e.g., 0 or 1, yes or no, true or false). The logistic regression
model for binary outcomes can be written as:

Where:

 p is the probability that the dependent variable is 1,

 β0,β1,β2,...,βn are the coefficients,
 X1,X2,...,Xn are the independent variables.

The logistic function transforms the linear equation into a value between 0 and 1,
representing the probability of a certain class.

4.5.2 MULTINOMIAL LOGISTIC REGRESSION

Multinomial logistic regression is used when the dependent variable has more than two
categories. Unlike binary logistic regression, which predicts probabilities for two classes,
multinomial logistic regression predicts probabilities for multiple classes (e.g., A, B, C). Each class
has its own logistic function, and the probability of an outcome belonging to a particular class is
calculated based on a set of logistic functions. The model is represented as:

Where pk is the probability of class k and p1 is the probability of a reference class. This
model is widely used in scenarios like predicting customer choices or categorizing text into multiple
classes.

4.5.3 MODEL EVALUATION METRICS

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 Several metrics are used to evaluate the performance of a logistic regression model,
especially in classification problems:

1. Accuracy: The proportion of correctly classified observations. It is a basic measure but may
not be useful in cases of imbalanced datasets.

2. Precision: The ratio of correctly predicted positive observations to the total predicted
positives. It is useful when the cost of false positives is high.

3. Recall (Sensitivity): The ratio of correctly predicted positive observations to all actual
positives. It is crucial when missing positive cases (false negatives) is more critical.

4. F1 Score: The harmonic mean of precision and recall. It balances the trade-off between
precision and recall and is useful when both false positives and false negatives are costly.

5. ROC-AUC (Receiver Operating Characteristic - Area Under Curve): This metric

evaluates the trade-off between the true positive rate and false positive rate across different
thresholds. A higher AUC indicates better model performance.

These metrics help assess how well the logistic regression model performs in classification tasks.

4.6 INTRODUCTION TO DECISION TREES

Decision trees are a popular supervised machine learning method used for classification and
regression tasks. They work by recursively splitting the data into subsets based on the value of input
features, resulting in a tree-like structure of decisions. Each internal node in the tree represents a
decision based on a feature, each branch represents the outcome of a decision, and each leaf node
represents the final classification or regression result.

Decision trees are intuitive, easy to interpret, and can handle both numerical and categorical
data, making them useful for a wide range of problems.

4.6.1 DECISION TREE CONSTRUCTION

Constructing a decision tree involves selecting the feature that best separates the data at each
step, typically based on a criterion like information gain or Gini impurity. The tree is built

14
recursively by splitting the dataset until all data points in a subset belong to the same class (for
classification) or until a stopping condition is reached (for regression).

The basic steps in decision tree construction are:

1. Select the Best Feature: Choose the feature that provides the most significant separation
between data points using criteria like information gain or Gini index.
2. Create Decision Nodes: Split the dataset into smaller subsets based on the selected feature.
3. Repeat the Process: Recursively apply the same process to the resulting subsets.
4. Stopping Condition: Stop splitting when a certain condition is met (e.g., maximum depth,
all samples in a node belong to the same class).

4.6.2 DECISION TREE ALGORITHM

Several algorithms are used to construct decision trees, the most common being:

1. ID3 (Iterative Dichotomiser 3): This algorithm uses information gain based on entropy to
select the best feature for splitting at each node. It continues building the tree until all
attributes are exhausted or the data is perfectly classified.
2. CART (Classification and Regression Trees): CART is used for both classification and
regression tasks. It splits the data using the Gini impurity for classification or the least
squared deviation for regression. Unlike ID3, CART builds binary trees where each split
produces exactly two branches.
3. C4.5: An extension of ID3, C4.5 handles both categorical and continuous features and uses
a concept of gain ratio (a normalized version of information gain) to split the nodes. It can
handle missing values and allows pruning after tree construction.

4.6.3 MODEL EVALUATION

Evaluating a decision tree model involves several metrics depending on whether the task is
classification or regression:

 Accuracy: The percentage of correct predictions (for classification tasks).

 Confusion Matrix: A table summarizing the model's performance, showing true positives,
true negatives, false positives, and false negatives.
 Precision, Recall, F1 Score: Common classification metrics that evaluate the performance
of the model on positive class predictions.
 Mean Squared Error (MSE): Used in regression tasks to calculate the average squared
difference between predicted and actual values.
 ROC-AUC: Evaluates the trade-off between true positive and false positive rates for
classification problems.

4.6.4 PRUNING

Pruning is a crucial step in decision tree optimization that helps reduce overfitting by
removing sections of the tree that are not significant. There are two main types of pruning:

1. Pre-pruning (Early Stopping): Stops the tree construction early, based on predefined
conditions like maximum depth, minimum samples per leaf, or maximum number of nodes.
2. Post-pruning: Involves growing the full tree first and then removing the least important
branches. Post-pruning techniques like cost-complexity pruning use a validation set to prune
branches that do not improve performance.
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 Pruning enhances the generalization ability of the model by reducing the complexity of the
tree, preventing overfitting to the training data.

5. ADVANCED MACHINE LEARNING ALGORITHMS

Advanced Algorithms and Applications in Machine Learning focus on developing
sophisticated techniques to solve complex problems more efficiently. These algorithms include deep
learning, reinforcement learning, ensemble methods (e.g., Random Forest, Gradient Boosting), and
optimization techniques like Genetic Algorithms and Simulated Annealing. They are applied in
areas such as natural language processing, computer vision, recommendation systems, and
autonomous systems. Advanced algorithms improve accuracy, scalability, and generalization,
making them essential for real-world applications in AI-driven industries.

5.1 INTRODUCTION TO K-NEAREST NEIGHBOURS (KNN)

K-Nearest Neighbors (KNN) is a simple, yet powerful, supervised learning algorithm used
for both classification and regression tasks. It is based on the idea that data points with similar
features tend to be near each other. The KNN algorithm works by identifying the "K" nearest
neighbors to a new data point and classifying it based on the majority label (for classification) or
averaging the labels (for regression). KNN is a non-parametric and lazy learning algorithm,
meaning it makes no prior assumptions about the data distribution and does not learn a model until
predictions are required.

5.1.1 KNN ALGORITHM

The KNN algorithm follows these steps:

1. Choose the value of K: Select the number of nearest neighbors (K). A smaller K can lead to
overfitting, while a larger K may smooth out the decision boundary but risk underfitting.
2. Calculate the Distance: For a new data point, calculate the distance to all training data
points using a distance metric such as Euclidean distance (most common), Manhattan
distance, or Minkowski distance.

3. Identify Neighbors: Select the K nearest neighbors (those with the smallest distances).
4. Voting or Averaging:

 Classification: Assign the new data point to the class that is most common
among its K neighbors (majority vote).
 Regression: Calculate the average of the values of the K neighbors to predict the
output.

5. Predict the Label: Return the predicted class label or continuous value.

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 Fig 1: K-NN Procedure

5.1.2 MODEL TRAINING AND PREDICTION

Unlike many algorithms, KNN does not involve an explicit training phase. Instead, KNN
stores the entire dataset and performs calculations only when making predictions. This makes
KNN a "lazy learner," as it defers the generalization of the data until prediction time.

For prediction, given a test sample, KNN computes the distance from the test sample to all
training samples, selects the K closest samples, and determines the output based on the majority
or average of those neighbors. While this process is simple, it can become computationally
expensive when the dataset is large, as KNN must compare the test sample to every training
sample.

5.1.3 KNN APPLICATIONS

KNN has a wide range of applications in various fields, including:

1. Pattern Recognition: Used to classify images, handwriting, and speech, as KNN can
identify patterns based on similarity.
2. Recommendation Systems: By comparing user preferences and recommending products,
movies, or services based on the preferences of similar users.
3. Medical Diagnosis: Applied in medical fields to predict diseases or classify patient
conditions based on similarities in medical records or symptoms.
4. Anomaly Detection: Detecting unusual patterns in data for fraud detection, network
security, or fault detection in machinery by finding data points that do not have enough
neighbors close by.
5. Text Classification: Applied in natural language processing (NLP) for spam detection,
sentiment analysis, and categorizing documents.

5.2 CORRELATION

Correlation is a statistical measure that describes the strength and direction of a relationship
between two variables. It quantifies how changes in one variable are associated with changes in
another. A correlation coefficient ranges from -1 to 1, where:
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  1 indicates a perfect positive correlation (both variables move in the same direction),
 -1 indicates a perfect negative correlation (one variable increases while the other decreases),
 0 indicates no correlation (no relationship between the variables).

5.2.1 PEARSON CORRELATION COEFFICIENT

The Pearson correlation coefficient (r) is the most commonly used measure of correlation
and assesses the linear relationship between two continuous variables. It assumes that the
variables are normally distributed and measures how much one variable changes as the other
changes. The formula for Pearson correlation is:

Where:

 X and Y are the two variables,

 Xˉ and Yˉ are the mean values of X and Y,
 r ranges from -1 to 1.

Pearson correlation is useful when you want to assess the strength and direction of a linear
relationship.

5.2.2 SPEARMAN CORRELATION COEFFICIENT

The Spearman correlation coefficient (ρ or "rho") is a non-parametric measure of rank

correlation. It assesses how well the relationship between two variables can be described by a
monotonic function. Unlike Pearson correlation, Spearman correlation does not assume that the
data is normally distributed, making it more robust for non-linear relationships or when data
contains outliers. The formula for Spearman’s rank correlation is:

Where:

 did_idi is the difference between the ranks of corresponding values,

 nnn is the number of data points.

Spearman is commonly used when the data is ordinal or when the relationship is not linear.

5.2.3 CORRELATION ANALYSIS AND INTERPRETATION

Correlation analysis is used to determine the strength and direction of relationships between
variables. When interpreting correlation results, consider the following guidelines:

 Positive correlation: As one variable increases, the other also increases (closer to +1).
 Negative correlation: As one variable increases, the other decreases (closer to -1).
 No correlation: The variables do not move together in any discernible way (closer to 0).

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 While correlation helps identify relationships, it does not imply causation. A strong
correlation between two variables does not necessarily mean that changes in one cause changes
in the other; there may be other factors or variables influencing the relationship.

Example:

 A Pearson correlation of 0.85 between hours studied and exam scores indicates a strong
positive relationship, meaning that more study hours are associated with higher exam scores.
 A Spearman correlation of -0.75 between rank in a race and finish time suggests a strong
negative relationship; the faster the finish time, the higher the rank.

5.3 CONFUSION MATRIX

A confusion matrix is a performance measurement tool for machine learning classification

models. It provides insight into the model's predictions by showing how well the predicted
classifications align with the actual classifications. The matrix is a table with four outcomes,
offering a comprehensive understanding of both correct and incorrect predictions, and is
structured as follows:

Predicted Positive Predicted Negative

Actual Positive True Positive (TP) False Negative (FN)
Actual Negative False Positive (FP) True Negative (TN)

True Positives (TP)

True Positives represent instances where the model correctly predicted the positive class.
For example, in a disease classification task, TP would be cases where the model correctly
identified patients as having the disease.

True Negatives (TN)

True Negatives are instances where the model correctly predicted the negative class. In the
same disease classification example, TN would be cases where the model correctly identified
patients as not having the disease.

False Positives (FP)

False Positives, also known as Type I errors, occur when the model incorrectly predicts the
positive class for an actual negative instance. This would be when a healthy patient is wrongly
classified as having the disease.

False Negatives (FN)

False Negatives, or Type II errors, happen when the model incorrectly predicts the negative
class for an actual positive instance. In this case, the model fails to detect the disease in a patient
who actually has it.

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5.3.1 ACCURACY

Accuracy is a measure of how often the model's predictions are correct, calculated as the
ratio of correct predictions (TP + TN) to the total number of predictions. It is useful when the
classes are balanced.

5.3.2 PRECISION

Precision measures the proportion of true positive predictions out of all positive predictions.
It is crucial when the cost of false positives is high.

5.3.3 RECALL

Recall, or Sensitivity, measures the proportion of true positive predictions out of all actual
positive instances. It is important in scenarios where missing positive instances (false negatives) is
costly.

5.3.4 F1 SCORE

The F1 Score is the harmonic mean of precision and recall, providing a single metric that
balances the trade-off between them. It is especially useful when the dataset is imbalanced, and both
false positives and false negatives are critical.

5.4 COMPUTER VISION (CV)

Computer Vision is a field of artificial intelligence (AI) that enables computers to interpret,
analyze, and understand visual information from the world, such as images and videos. It aims
to replicate human vision and automate tasks that require visual understanding. By extracting
and processing visual data, computer vision systems can perform tasks like image classification,
object detection, face recognition, and even self-driving car navigation.

Computer vision has rapidly evolved due to advancements in machine learning, especially
deep learning, which allows models to learn from vast amounts of visual data. It plays a critical
role in various sectors, including healthcare, retail, automotive, and robotics.

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 Fig 2: Computer Vision

5.4.1 IMAGE PROCESSING TECHNIQUES

Image processing involves manipulating and analyzing images to improve their quality or
extract useful information. Some commonly used techniques in image processing include:

1. Image Smoothing: This technique reduces noise and variations in an image, making it
clearer. Methods such as Gaussian Blurring or Median Filtering are used to smoothen
images by reducing unnecessary details.
2. Edge Detection: Detecting the boundaries of objects in images is crucial for identifying
shapes. Algorithms like Canny Edge Detection and Sobel Filters are widely used to detect
sharp transitions in pixel intensity, highlighting the edges of objects.
3. Thresholding: Thresholding converts grayscale images into binary images (black and
white) by setting a pixel intensity threshold. Pixels above the threshold are set to white, and
those below are set to black. This technique is used in applications like document scanning
and segmentation.
4. Morphological Operations: These operations, such as Erosion and Dilation, are used to
remove small objects or enhance structures within an image. They are commonly applied in
preprocessing tasks, such as noise removal or shape enhancement.
5. Image Segmentation: Image segmentation divides an image into meaningful regions or
segments. Watershed Segmentation and K-means Clustering are popular techniques to
separate objects from the background or other regions.

5.4.2 OBJECT DETECTION AND RECOGNIZATION

Object Detection involves locating and identifying objects within an image or video. It not
only classifies objects but also predicts their positions using bounding boxes. Object
Recognition, on the other hand, goes a step further by identifying specific instances of objects,
such as recognizing a particular face in a crowd or detecting a specific brand of a product.

Popular techniques for object detection and recognition include:

1. Convolutional Neural Networks (CNNs): CNNs are the backbone of modern object
detection models. They can automatically learn features from images and are particularly
effective in tasks like image classification and face detection.
2. YOLO (You Only Look Once): YOLO is a real-time object detection algorithm that
divides an image into grids and predicts bounding boxes and class probabilities for objects

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 in a single pass through the network. It is widely used for tasks that require real-time object
detection, such as video surveillance or autonomous driving.
3. R-CNN (Region-based CNN): R-CNN family algorithms (e.g., Fast R-CNN, Faster R-
CNN) are another set of powerful object detection models. They first generate potential
object regions (proposals) and then classify each region using a CNN.
4. Haar Cascades: Haar Cascade classifiers are an older but efficient method for object
detection, especially in detecting faces. They work by applying multiple stages of classifiers
to an image to detect objects in real-time.

5.4.3 COMPUTER VISION APPLICATIONS

Computer vision is transforming numerous industries with a wide array of applications,

including:

1. Healthcare: In medical imaging, computer vision is used to detect tumors, diagnose

diseases, and assist in surgeries. Techniques like image segmentation help in analyzing X-
rays, MRIs, and CT scans to identify abnormalities.
2. Autonomous Vehicles: Self-driving cars rely on computer vision to interpret the
surroundings by detecting pedestrians, other vehicles, traffic signs, and road conditions in
real-time. Object detection algorithms ensure safe navigation.
3. Face Recognition: Facial recognition technology is widely used in security systems,
unlocking devices, and even social media tagging. Models trained on facial features can
identify individuals in images or videos with high accuracy.
4. Retail: In retail, computer vision enables automated checkout systems, inventory
management, and customer behavior analysis. AI-powered cameras can track products on
shelves and identify when restocking is needed.
5. Agriculture: Computer vision is used in precision agriculture to monitor crops, detect
diseases, and analyze soil health. Drones equipped with computer vision technologies can
survey large farmlands and provide insights to farmers.
6. Augmented Reality (AR): In AR applications, computer vision helps in overlaying digital
content onto the physical world. This is used in mobile apps, gaming, and even navigation
systems to enhance the user's real-world experience.

5.5 CONVOLUTIONAL NEURAL NETWORKS (CNN)

Convolutional Neural Networks (CNNs) are a class of deep learning models specifically
designed for analyzing visual data, such as images and videos. Inspired by the human visual
system, CNNs are highly effective in identifying patterns, textures, and objects in images,
making them the backbone of modern computer vision tasks. Unlike traditional neural networks,
CNNs take advantage of the spatial structure of images, allowing them to process data with a
grid-like topology efficiently.

CNNs are widely used in applications such as image classification, object detection, face
recognition, and even natural language processing. By using convolutional layers, CNNs can
automatically learn hierarchical feature representations, from simple features like edges and
corners to more complex features like shapes and objects.

5.5.1 CNN ARCHITECTURE

A typical CNN consists of several key layers, each contributing to the extraction and
processing of image features:

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 1. Convolutional Layers: The convolutional layer is the core building block of CNNs. It
applies filters (or kernels) to the input image to detect features like edges, textures, and
patterns. This layer generates feature maps, which highlight the presence of specific features
across the image. Each filter produces a different feature map, helping the network capture
various aspects of the image.
2. Pooling Layers: Pooling layers are used to down-sample the feature maps, reducing their
spatial dimensions and computational complexity while preserving important features.
Common pooling techniques include Max Pooling (which selects the maximum value from
a window of the feature map) and Average Pooling. Pooling helps make the model invariant
to small translations in the input image, ensuring that the model can detect features
regardless of their location.
3. Fully Connected Layers (FC Layers): After the convolutional and pooling layers, the
network typically includes one or more fully connected layers. These layers take the high-
level features extracted by the convolutional layers and map them to the output classes. In an
image classification task, the final fully connected layer typically outputs a probability
distribution over the possible classes.
4. Activation Functions: Activation functions such as ReLU (Rectified Linear Unit) introduce
non-linearity into the network, allowing it to learn complex patterns. Without these non-
linear functions, the network would behave like a simple linear model.
5. Softmax Layer: In classification tasks, the final layer of a CNN is usually a Softmax layer,
which converts the output into probabilities for each class.

Fig 3: CNN Procedure

5.5.2 TRANSFER LAERNING WITH PRE-TRAINDED MODELS

Transfer learning is a technique where a model that has been pre-trained on a large dataset is
fine-tuned on a smaller, domain-specific dataset. In the context of CNNs, popular pre-trained
models such as VGG, ResNet, Inception, and MobileNet are trained on massive datasets like
ImageNet, which contains millions of labeled images across thousands of categories.

By leveraging transfer learning, the pre-trained model’s convolutional layers can be used to
extract relevant features from a new dataset, while the fully connected layers can be re-trained
to suit the specific task. This approach saves time and computational resources and often leads

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 to improved performance, particularly when the target dataset is small or similar to the pre-
trained dataset.

Transfer learning is especially effective in scenarios where labeled data is scarce, as the pre-
trained model already has a good understanding of general features like edges, textures, and
shapes. Fine-tuning these models can significantly reduce the need for extensive training and
improve the performance of image classification tasks.

5.6 NATURAL LANGUAGE PROCESSING (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses
on the interaction between computers and humans through natural language. The primary goal
of NLP is to enable machines to understand, interpret, and generate human language in a
meaningful way. It encompasses various tasks, including language translation, sentiment
analysis, text summarization, and conversational agents like chatbots.

NLP combines computational linguistics, machine learning, and linguistics to analyze and
understand human language. With the increasing volume of text data generated daily—from
social media, emails, articles, and customer reviews—NLP has become essential for businesses
and researchers seeking to extract insights from unstructured text. Advances in deep learning
and the availability of large datasets have significantly improved NLP models, enabling them to
achieve impressive performance in tasks such as text classification and language generation.

5.6.1 TEXT PREPROCESSING TECHNIQUES

Text preprocessing is a crucial step in NLP that involves transforming raw text into a clean
and structured format suitable for analysis. This step helps to improve the performance of NLP
models by reducing noise and standardizing the input data. Common text preprocessing
techniques include:

1. Tokenization: Tokenization is the process of breaking down text into smaller units, called
tokens. Tokens can be words, phrases, or sentences, depending on the granularity required
for analysis. For example, the sentence "Natural Language Processing is fascinating!" can be
tokenized into ["Natural", "Language", "Processing", "is", "fascinating", "!"].
2. Lowercasing: Converting all text to lowercase helps to ensure consistency by treating words
with different casing (e.g., "NLP" and "nlp") as the same token.
3. Removing Punctuation and Special Characters: Punctuation marks and special characters
often do not contribute to the meaning of the text and can be removed to simplify analysis.
For instance, "Hello, world!" would become "Hello world".
4. Stopword Removal: Stopwords are common words like "and," "the," and "is" that often do
not add significant meaning to the text. Removing stopwords can reduce the dimensionality
of the data and improve model performance.
5. Stemming and Lemmatization: Stemming involves reducing words to their base or root
form (e.g., "running" to "run"), while lemmatization considers the context and converts
words to their dictionary form (e.g., "better" to "good"). Both techniques help standardize
words and reduce variations.

5.6.2 TEXT REPRESENTATION

Once text has been preprocessed, the next step is to represent it in a format that machine
learning models can understand. Several text representation techniques include:

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 1. Bag of Words (BoW): The Bag of Words model represents text as an unordered collection
of words, disregarding grammar and word order. Each document is converted into a vector
of word frequencies, where each dimension corresponds to a unique word in the vocabulary.
While simple, BoW does not capture word context or relationships.
2. Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF improves upon BoW
by weighing the frequency of words relative to their importance in a collection of
documents. It assigns higher weights to words that are frequent in a particular document but
rare across the entire corpus, helping to identify key terms.
3. Word Embeddings: Word embeddings, such as Word2Vec, GloVe, and FastText, represent
words as dense vectors in a continuous vector space. These embeddings capture semantic
relationships and contextual meanings, allowing similar words to have similar vector
representations. For example, the words "king" and "queen" would have embeddings that
reflect their relationship in context.
4. Transformers: Transformers, particularly models like BERT and GPT, have revolutionized
text representation by using self-attention mechanisms to capture contextual information and
relationships between words in a sentence. These models have achieved state-of-the-art
performance in various NLP tasks.

5.6.3 SENTIMENT ANALYSIS AND TEXT CLASSIFICATION

Sentiment analysis is an NLP task that involves determining the sentiment or emotional tone
of a piece of text. It is widely used in applications like social media monitoring, customer
feedback analysis, and brand reputation management. Sentiment analysis can be categorized
into:

1. Binary Sentiment Analysis: This involves classifying text as either positive or negative.
For example, a review stating, "I love this product!" would be classified as positive.
2. Multi-class Sentiment Analysis: This expands on binary sentiment analysis by including
neutral or multiple sentiment categories (e.g., positive, negative, neutral). This approach
provides a more nuanced understanding of public opinion.
3. Aspect-Based Sentiment Analysis: This advanced technique analyzes sentiments towards
specific aspects of a product or service. For instance, in the sentence "The battery life is
great, but the camera quality is poor," the sentiment is positive towards battery life and
negative towards camera quality.

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 6. CONCLUSION

The internship provided a comprehensive understanding of Python programming and its

applications in machine learning, specifically focusing on linear regression and its role in predicting
housing prices. Python's flexibility and wide range of libraries such as Pandas, NumPy, and Scikit-
learn enabled efficient data manipulation, analysis, and model building.

Through this project, I gained practical experience in data collection, preprocessing, model
development, and evaluation, essential steps in the machine learning workflow. The house price
prediction task demonstrated how machine learning models can uncover valuable insights from data
and provide accurate predictions based on historical patterns. By applying supervised learning
techniques like linear regression, I was able to develop a simple yet effective model to predict
housing prices based on various features.

This not only deepened my understanding of fundamental machine learning concepts but also
enhanced my skills in handling real-world datasets, building predictive models, and evaluating their
performance.

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 7. ANNEXURE

HOUSING PRICE PREDICTION MODEL

7.1 INTRODUCTION

The Housing Price Prediction Model aims to predict the price of houses based on various
features such as location, size, number of bedrooms, and other factors. By utilizing machine
learning algorithms, particularly linear regression, the model analyzes historical data to establish
a relationship between housing features and their respective prices. This project showcases how
machine learning can be applied in the real estate market to make informed decisions based on
data patterns.

7.2 PURPOSE

The primary purpose of this project is to develop a model that accurately predicts house
prices using regression techniques. By feeding historical housing data into the model, it will
learn the relationship between various features and the house prices, enabling it to predict the
price of new houses based on their features. This helps buyers, sellers, and real estate agents
make more data-driven decisions.

7.3 PROCEDURE

1. Data-Collection:
We collect housing data from reliable sources, including features like house size, number of
rooms, location, and price.
2. Data Preprocessing :
Handling Missing Values: Any missing values in the dataset are handled by either filling them
with mean/median or removing them.
Feature Scaling: Standardization or normalization of features is performed to ensure they are
on the same scale, which improves model accuracy.
Encoding Categorical Variables: Categorical variables (e.g., location) are converted into
numerical representations using techniques like one-hot encoding.

3. Splitting Data :
The dataset is split into two parts: training and testing sets. Typically, 80% of the data is
used for training, and 20% is used for testing.

4. Model Building :

The Linear Regression algorithm is applied to the training data. The model learns the
relationship between the features and the house prices during this phase.

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5. Model Evaluation :

The trained model is evaluated using the testing dataset. Evaluation metrics such as Mean
Squared Error (MSE) and R-squared (R²) are used to measure how well the model performs.

6. Prediction :

The model is used to predict the prices of houses based on the input features

7.4 IMPLEMENTATION

1. INSTALL LIBRARIES
Ensure that you have the required libraries installed in your Python environment.
pip install pandas numpy scikit-learn jupyter.

2. CREATE JUPYTER NOTEBOOK

Run the following command in cmd to start Jupyter Notebook:

jupyter notebook

This will open the Jupyter interface in your browser. You can create a new notebook by clicking
on "New" and selecting "Python 3."

Fig 1: Jupyter Interface

3. CREATE CODE SNIPPET CELLS

Import Libraries (Code Cell)

Import the necessary libraries for data handling, model building, and evaluation.

# Importing the required libraries

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

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 from sklearn.metrics import mean_squared_error, r2_score

Load and Explore the Dataset (Code Cell)

Load your housing dataset (assumed to be in CSV format) and perform basic exploration.

# Load the housing dataset (replace 'housing.csv' with your dataset path)

data = pd.read_csv('housing.csv')

# Display the first few rows of the dataset

data.head()

Data Preprocessing (Code Cell)

Handle missing values and split the dataset into features and target variables.

# Handle missing values

data.fillna(data.mean(), inplace=True)

# Split the dataset into features (X) and target variable (y)

X = data.drop('Price', axis=1) # Features

y = data['Price'] # Target variable

Train-Test Split (Code Cell)

Split the dataset into training and testing sets.

# Splitting the dataset into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Building and Training (Code Cell)

Initialize and train the Linear Regression model.

# Initialize the Linear Regression model

model = LinearRegression()

# Train the model on the training data

model.fit(X_train, y_train)

Model Evaluation (Code Cell)

Evaluate the model using the test data.

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 # Predict house prices using the test data

y_pred = model.predict(X_test)

# Evaluate the model performance

mse = mean_squared_error(y_test, y_pred)

r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")

print(f"R-squared Score: {r2}")

Prediction for New Data (Code Cell)

Use the trained model to make predictions for new house data.

# Predict the price of a new house with features [2000, 3, 2, 1]

new_house = np.array([[2000, 3, 2, 1]]) # Example: [size, bedrooms, bathrooms, floors]

predicted_price = model.predict(new_house)

print(f"Predicted Price for the new house: {predicted_price}")

Execution :

1. Save the Notebook: After writing all the cells, save the notebook (File -> Save As) and run
the cells (Cell -> Run All).
2. Running the Notebook: You can run each cell one by one by clicking the "Run" button, or
you can run all cells at once (Cell -> Run All).
3. Exporting the Notebook: You can export the notebook as a PDF or HTML by going to File
-> Download As and selecting your preferred format.

7.5 TESTCASES

1. Test Case 1: Basic Prediction

 Input: Features [2000, 3, 2, 1] representing a house of 2000 square feet with 3
bedrooms, 2 bathrooms, and 1 floor.
 Expected Output: Predicted price (e.g., 450,000).
2. Test Case 2: Model Evaluation
 Input: Test dataset (automatically selected from the dataset).
 Expected Output: Mean Squared Error and R-squared score indicating the
performance of the model.
3. Test Case 3: Missing Values Handling
 Input: Dataset with missing values in some columns.
 Expected Output: The missing values are handled, and the model should still
function without errors.
4. Test Case 4: Prediction for Different House Features
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  Input: Features [1500, 2, 1, 1] representing a house of 1500 square feet with 2
bedrooms, 1 bathroom, and 1 floor.
 Expected Output: A predicted price (e.g., 320,000).

7.6 RESULTS

Fig 2: Results

The dataset used for the housing price prediction model contains key features such as
median income (`MedInc`), house age (`HouseAge`), average rooms (`AveRooms`), bedrooms
(`AveBedrms`), population (`Population`), occupancy (`AveOccup`), geographic coordinates
(`Latitude`, `Longitude`), and the target variable, housing price (`PRICE`). The data is
complete with no missing values, ensuring consistency for model training. The dataset was split
into a training set of 16,512 records and a test set of 4,128 records.

The model's performance was evaluated using Mean Squared Error (MSE), which was
calculated as 0.555, and the R-squared score was 0.576, indicating the model explained 57.6%
of the variance in housing prices.

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