Introduction
Welcome to our latest blog post! Today, we're excited to introduce a new project requirement entitled "Regression, Clustering, and Classification Strategies for Informed Decision-Making." In this post, we will delve into three key tasks: Regression, Clustering, and Classification. Additionally, we will explore the Solution Approach section, detailing our proposed methods for addressing this project requirement. We'll guide you through our thought process, the methodologies we intend to utilize, and the tools at our disposal. Our aim is to offer a comprehensive solution that is both effective and efficient.
Then, we will showcase the output of our analysis, including visualizations, key findings, and interpretations of the data.
Project Requirement
Project Requirements Overview
This assessment is designed to test your ability to use various concepts & technologies in AI. You are asked to write Python programs to complete the tasks outlined, and prepare a document (MS Word or LaTeX) containing all the associated information for each task. Think of yourself as an AI/Data Science specialist tasked with building a machine learning system for a client who wants to make informed decisions based on data. Therefore, focus on providing a clear description of the software developed, including the design cycle, analysis of the dataset, and the model's effectiveness (accuracy scores, etc.).
Task 1: Regression
In this task you are required to apply a machine learning algorithm to the data set houseprice_data.csv which can be downloaded from the assignment task on canvas. This data set contains information about house sales in King County, USA. The data has 18 features, such as: number of bedrooms, bathrooms, floors etc., and a target variable: house price.
Using linear regression (simple or multiple), develop a model to predict the price of a house. After developing the model you should also analyse the results and discuss the effectiveness of the model, outlining the improvements when developing the model.
Ideas to consider when completing this task:
● Is there a way of visualising your model? (Possibly just one or two input/feature
● variable(s).)
● How will you assess the effectiveness of the model?
● Include as many features as you can. Does the model improve?
● How could you make further improvements?
● What can you conclude about your model?
Task 2: Clustering
In this task you are required to apply a machine learning algorithm to the data set country_data.csv which can be downloaded from the assignment task on canvas. This data set contains information about a countries child mortality, exports, health spending, etc.
Use clustering to investigate this data set. After clustering the data you should analyse the results and discuss what can be concluded by the clusters.
Ideas to consider when completing this task:
● Is there a way of visualising the clusters?
● Can you make any conclusions about the clustering?
● Include as many features as you can. Does the clustering change?
● What advice would you give, in the context of the data, based on the clustering?
Task 3: Classification & Neural Networks
In this task you are required to apply a variety of machine learning algorithms to the data set nba_rookie_data.csv which can be downloaded from the assignment task on canvas. This data set contains NBA rookie performance with target variable Target_5Yrs with 1: if career length >= 5 yrs or 0: if career length < 5 yrs. The classification problem here is to predict if a player will last 5 years in the NBA.
Apply Logistic Regression, Gaussian Naive Bayes and construct Neural Networks. After developing the various models you should also analyse the results and discuss the effectiveness of the models, outlining the improvements when developing the models and compare the approaches/algorithms used (strengths and weaknesses).
Ideas to consider when completing this task:
● Apply various algorithms to the problem. Caution: Use a small number rather
● than many, analyse in depth rather than being superficial and repetitive.
● Is there a way of visualising the model(s)?
● How will you assess the effectiveness of the model(s)?
● Include as many features as you can. Does the model improve?
● Compare the models produced.
● How could you make further improvements?
● What can you conclude about your model?
● How strong is the relationship between the predictor and target variables?
Task 4: Ethics of AI
In this task you are required to write a short essay (< 500 words) to discuss the following ethical dilemma in AI:
The Trolley Problem is a well known problem in ethics, discuss the trolley problem
in the context of autonomous vehicles.
Marks will be awarded based on how well you meet the three criteria:
● Understanding - In-depth, authoritative, full understanding of key issues with evidence of originality.
● Depth of knowledge - Key issues analysed, selective source(s) used to support argument/discussion.
● Structure - Coherent and compelling work logically presented.
Solution Approach :
Task 1. Regression Analysis for House Price Prediction
Dataset Used:
● House price data from King County, USA.
● Features include the number of bedrooms, bathrooms, floors, etc.
● Target variable: house price.
Basic Data Information:
● Checking data info: total entries, columns, data types.
● Statistical summary: mean, standard deviation, min/max values.
Data Processing Techniques:
● Handling duplicate data: checking and removing duplicates.
● Scaling features: using StandardScaler for better model performance.
Feature Selection:
● Initial features exploration: identifying skewed features like 'price'.
● Log transformation: correcting skewness in the target variable.
● Correlation analysis: selecting relevant features based on correlation matrix.
Testing and Training:
● Train/test split: dividing data into training and validation sets (80:20).
● Model evaluation metrics: R-squared (r2) score, Root Mean Squared Error (RMSE).
Algorithms Used:
● Baseline Model: Linear Regression for initial prediction.
● Model Tuning:
○ Model 1: Improved performance after log transformation and feature selection.
○ Model 2: Further feature selection based on correlation, maintaining performance with fewer features.
Evaluation Used:
● Comparing model performances: baseline vs. tuned models.
● Metrics comparison: RMSE and r2 scores for model selection.
Task 2: Clustering Analysis for Country Data
Dataset Used:
● Country data containing various socio-economic indicators.
● Features include child mortality rate, income, health spending, etc.
● Utilized pandas for data loading and exploration.
Basic Data Information:
● Checked data info: total entries, columns, data types.
● Statistical summary: mean, standard deviation, min/max values.
● Addressed anomalies: removed rows with numeric values for countries.
Data Processing Techniques:
● Univariate analysis: identified outliers and distributions for each feature.
● Bivariate analysis: explored relationships between income and other indicators.
● Capped outliers instead of removal to retain country-specific data.
Feature Selection:
● Used socio-economic indicators for clustering analysis.
● Standardized features using StandardScaler for clustering accuracy.
Testing and Training:
● Elbow method: Determined optimal K value for KMeans clustering.
● Evaluated clusters based on socio-economic indicators.
● Visualized cluster profiles for interpretation and insights.
Algorithms Used:
● KMeans clustering with varying K values (3 and 4) for comparison.
● Identified cluster profiles and characteristics for each K value.
Evaluation Used:
● Analyzed cluster distributions and average indicators per cluster.
● Compared cluster profiles between different K values (3 and 4).
● Interpreted insights from cluster analysis for informed decision-making.
Task 3 : Classification Analysis for NBA Rookie Data
Dataset Used:
● NBA rookie data containing various performance indicators.
● Features include points, rebounds, assists, etc.
● Utilized pandas for data loading and exploration.
Basic Data Information:
● Checked data info: total entries, columns, data types.
● Addressed missing values by dropping them from the dataset.
● Statistical summary: mean, standard deviation, min/max values.
Data Preprocessing:
● Handled class imbalance using SMOTE to upsample the minority class.
● Standardized features using MinMaxScaler for consistent scaling.
● Reduced dimensionality using PCA to capture 95% variance.
Testing and Training:
● Split data into training and validation sets using train_test_split.
● Utilized Logistic Regression, Gaussian Naive Bayes, and MLP Classifier for classification.
● Evaluated models using accuracy score and confusion matrix.
Algorithms Used:
● Logistic Regression: Baseline model and grid search for parameter tuning.
● Gaussian Naive Bayes: Baseline model for probabilistic classification.
● MLP Classifier (Neural Network): Baseline model and grid search for hyperparameter tuning.
Evaluation Used:
● Assessed model performance using accuracy scores and confusion matrices.
● Identified best hyperparameters using GridSearchCV for MLP Classifier.
● Displayed model accuracies and confusion matrices for comparison.
Task 4 :
We will prepare a comprehensive project report that encompasses the tasks discussed above.
Some Output :
In our project focusing on "Regression, Clustering, and Classification Strategies for Informed Decision-Making," we've embarked on an enlightening journey exploring diverse datasets and employing cutting-edge methodologies to extract actionable insights. Through meticulous data preprocessing, robust modeling techniques, and thorough evaluation, we've deciphered complex patterns and trends crucial for informed decision-making. Our endeavor began with regression analysis, where we leveraged machine learning algorithms to predict house prices in King County, USA. By meticulously assessing model effectiveness through metrics like R-squared scores and Root Mean Squared Error (RMSE), we gained valuable insights into housing market dynamics and identified areas for model refinement and improvement.
Transitioning to clustering analysis, we delved into socio-economic indicators of countries, unraveling hidden patterns through algorithms like KMeans. By visualizing cluster profiles and analyzing average indicators per cluster, we unearthed distinct socio-economic archetypes, empowering stakeholders with actionable intelligence for strategic decision-making. Finally, in our classification analysis of NBA rookie data, we tackled the challenging task of predicting players' career longevity using a variety of machine learning algorithms. Through rigorous model evaluation and comparison, we discerned the strengths and weaknesses of different approaches, providing stakeholders with invaluable insights into player performance and potential career trajectories. Overall, our project underscores the transformative power of data-driven strategies in fostering informed decision-making across diverse domains.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.