Harsh RB

Data Analytics Lab

4 min read

Course Objectives

  1. To explore the foundational principles of data analytics.
  2. To understand and apply statistical analysis methods.
  3. To discover patterns in data, analyze supervised and unsupervised models, and evaluate algorithm accuracy.
  4. To implement search methods and visualization techniques effectively.

Course Outcomes

Upon completion of this course, students will be able to:

  1. Understand and implement linear regression and logistic regression.
  2. Analyze and compare the functionality of various classifiers.
  3. Apply visualization techniques using different types of graphs.
  4. Use descriptive and predictive analytics for diverse datasets.

List of Experiments

1. Data Preprocessing

Perform the following tasks on a dataset:

  • a) Handling Missing Values: Impute or remove missing values.
  • b) Noise Detection and Removal: Identify and handle noisy data.
  • c) Identifying Data Redundancy and Elimination: Remove duplicate or redundant data.

Example Code Snippet:

import pandas as pd
 
# Load dataset
data = pd.read_csv("data.csv")
 
# Handle missing values
data.fillna(data.mean(), inplace=True)
 
# Remove duplicates
data.drop_duplicates(inplace=True)
 
# Noise removal (example: removing outliers)
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]

2. Implement Any One Imputation Model

Use mean, median, or KNN imputation to handle missing values.

Example Code Snippet:

from sklearn.impute import KNNImputer
 
imputer = KNNImputer(n_neighbors=2)
data_imputed = imputer.fit_transform(data)
print(data_imputed)

3. Implement Linear Regression

Build a linear regression model to predict continuous values.

Example Code Snippet:

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
 
X = data[['feature1', 'feature2']]
y = data['target']
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
 
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

4. Implement Logistic Regression

Build a logistic regression model for binary classification.

Example Code Snippet:

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
 
model = LogisticRegression()
model.fit(X_train, y_train)
 
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

5. Implement Decision Tree Induction for Classification

Build a decision tree classifier and visualize the tree structure.

Example Code Snippet:

from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
 
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
 
plt.figure(figsize=(12, 8))
plot_tree(model, filled=True, feature_names=['feature1', 'feature2'], class_names=['Class 0', 'Class 1'])
plt.show()

6. Implement Random Forest Classifier

Build a random forest classifier and evaluate its performance.

Example Code Snippet:

from sklearn.ensemble import RandomForestClassifier
 
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
 
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

7. Implement ARIMA on Time Series Data

Apply ARIMA (AutoRegressive Integrated Moving Average) for time series forecasting.

Example Code Snippet:

from statsmodels.tsa.arima.model import ARIMA
 
# Fit ARIMA model
model = ARIMA(data['value'], order=(5, 1, 0))
model_fit = model.fit()
 
# Forecast
forecast = model_fit.forecast(steps=10)
print(forecast)

8. Object Segmentation Using Hierarchical-Based Methods

Perform hierarchical clustering for object segmentation.

Example Code Snippet:

from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
 
cluster = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')
labels = cluster.fit_predict(data)
 
plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='rainbow')
plt.show()

9. Perform Visualization Techniques

Create various types of graphs (Bar, Column, Line, Scatter, 3D Cubes).

Example Code Snippet:

import matplotlib.pyplot as plt
import seaborn as sns
 
# Bar Chart
sns.barplot(x='category', y='value', data=data)
plt.show()
 
# Scatter Plot
plt.scatter(data['feature1'], data['feature2'])
plt.show()

10. Perform Descriptive Analytics on Healthcare Data

Analyze healthcare data using descriptive statistics.

Example Code Snippet:

print(data.describe())
sns.boxplot(x='age', y='health_status', data=data)
plt.show()

11. Perform Predictive Analytics on Product Sales Data

Predict future sales using regression or time series models.

Example Code Snippet:

# Use Linear Regression or ARIMA for prediction

12. Apply Predictive Analytics for Weather Forecasting

Forecast weather conditions using time series or machine learning models.

Example Code Snippet:

# Use ARIMA or LSTM for weather forecasting

Textbooks

  1. Student’s Handbook for Associate Analytics – II, III.
  2. Data Mining Concepts and Techniques by Han, Kamber (3rd Edition).

Reference Books

  1. Introduction to Data Mining by Tan, Steinbach, Kumar.
  2. Data Mining Analysis and Concepts by M. Zaki and W. Meira.
  3. Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D Ullman.

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