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 <p>This repository contains personal projects I did in my free time and during my CS master's degree.</p>
 
-### [Kaggle Natural Language Processing with Disaster Tweets Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_NLP_Disaster_Tweets) (11/2023)
+#### [Kaggle Natural Language Processing with Disaster Tweets Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_NLP_Disaster_Tweets) (11/2023)
 <details>
   <summary>Description</summary>
 The challenge revolved around constructing a machine learning model adept at distinguishing genuine disaster-related tweets from others. </details>
 
-### [Kaggle Digit Recognizer Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Digit_Recognizer) (10/2023)
+#### [Kaggle Digit Recognizer Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Digit_Recognizer) (10/2023)
 <details>
   <summary>Description</summary>
 The challenge entailed the accurate identification of digits from a dataset comprising tens of thousands of handwritten images. </details>
 
-### [Kaggle Forecasting Mini-Course Sales Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Forecasting_Mini-Course_Sales) (07/2023)
+#### [Kaggle Forecasting Mini-Course Sales Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Forecasting_Mini-Course_Sales) (07/2023)
 <details>
   <summary>Description</summary>
 This competition involved forecasting sales with synthetically generated time series datasets. </details>
 
-### [Kaggle ConnectX RL Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_ConnectX_ML_Competition) (07/2023) 
+#### [Kaggle ConnectX RL Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_ConnectX_ML_Competition) (07/2023) 
 <details>
   <summary>Description</summary>
 The ConnectX competition involves training an AI agent to play Connect4, and contestants are required to submit their agent in a Python file for evaluation on the leaderboard.</details>
 
-### [Exploratory data analysis on Kaggle Datasets](https://github.com/pelinkeskin/Personal_projects/tree/main/EDAonKaggleDatasets) (07/2023) 
+#### [Exploratory data analysis on Kaggle Datasets](https://github.com/pelinkeskin/Personal_projects/tree/main/EDAonKaggleDatasets) (07/2023) 
 <details>
   <summary>Description</summary>
 This folder contains notebooks I created for exploratory data analysis and visualization of interesting datasets obtained from Kaggle. </details>
 
-### [Kaggle Titanic ML Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Titanic_ML_Competition) (06/2023)
+#### [Kaggle Titanic ML Competition](https://github.com/pelinkeskin/Personal_projects/tree/main/Kaggle_Titanic_ML_Competition) (06/2023)
 <details>
   <summary>Description</summary>
 Titanic competition is about developing a machine learning model that makes binary classifications from tabular data of Titanic's passengers. </details> 
 
-### [AI_flappybird_player_Deep_Reinforcement_Learning](https://github.com/pelinkeskin/Personal_projects/tree/main/AI_flappybird_player_Deep_Reinforcement_Learning) (04/2023)
+#### [AI_flappybird_player_Deep_Reinforcement_Learning](https://github.com/pelinkeskin/Personal_projects/tree/main/AI_flappybird_player_Deep_Reinforcement_Learning) (04/2023)
 <details>
   <summary>Description</summary>
 The project involved training an AI agent who plays the Flappy Bird game using deep reinforcement learning. </details>
 
-### [Building an Interactive dashboard with Vega-Lite to visualize nuclear explosions worldwide](https://github.com/pelinkeskin/Personal_projects/tree/main/Interactive_dashboard_Vega-Lite) (04/2023)
+#### [Building an Interactive dashboard with Vega-Lite to visualize nuclear explosions worldwide](https://github.com/pelinkeskin/Personal_projects/tree/main/Interactive_dashboard_Vega-Lite) (04/2023)
 <details>
   <summary>Description</summary>
 I created an interactive dashboard using Vega-Lite to visualize nuclear explosions worldwide.</details>
 
-### [Deep Learning: Building CNN to recognize monkey species from disparate images](https://github.com/pelinkeskin/Personal_projects/tree/main/image_classification_with_CNN) (03/2023)
+#### [Deep Learning: Building CNN to recognize monkey species from disparate images](https://github.com/pelinkeskin/Personal_projects/tree/main/image_classification_with_CNN) (03/2023)
 <details>
   <summary>Description</summary>
 The project involved building a deep convolutional neural network model to recognize monkey species from disparate images. </details>
 
-### [Building Gradient Boosting Regressor from Scratch with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/self-built_GradientBoosting_Regressor) (03/2023)
+#### [Building Gradient Boosting Regressor from Scratch with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/self-built_GradientBoosting_Regressor) (03/2023)
 <details>
   <summary>Description</summary>
 The project involved creating a gradient-boosting regressor for continuous prediction problems by inheriting from appropriate scikit-learn base classes. </details>
 
-### [A small study to assess word frequencies across data analytics job ads](https://github.com/pelinkeskin/Personal_projects/tree/main/JobAdsWordFreqEval) (12/2022)
+#### [A small study to assess word frequencies across data analytics job ads](https://github.com/pelinkeskin/Personal_projects/tree/main/JobAdsWordFreqEval) (12/2022)
 <details>
   <summary>Description</summary>
 Data mining and text analysis for gaining insight into the common traits employers currently look for in data analytics roles. </details>
 
-### [Building Multi-Layer Neural Network from Scratch with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/Multi-Layer_Neural_Network_from_Scratch) (12/2022)
+#### [Building Multi-Layer Neural Network from Scratch with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/Multi-Layer_Neural_Network_from_Scratch) (12/2022)
 <details>
   <summary>Description</summary>
 I implemented a Multi-Layer Neural Network from Scratch with Python without using any ML library. </details>
 
-### [Analysing Books with Hadoop & Data Exploration with PySpark](https://github.com/pelinkeskin/Personal_projects/tree/main/Big_Data_Exploration_Hadoop_PySpark) (12/2022)
+#### [Analysing Books with Hadoop & Data Exploration with PySpark](https://github.com/pelinkeskin/Personal_projects/tree/main/Big_Data_Exploration_Hadoop_PySpark) (12/2022)
 <details>
   <summary>Description</summary>
 This is a combination of two projects for using big data management tools, Hadoop and Spark. </details>
 
-### [Building Time-Related Feature Engineering Pipelines with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/Time-Related_Feature_Engineering) (11/2022)
+#### [Building Time-Related Feature Engineering Pipelines with Python](https://github.com/pelinkeskin/Personal_projects/tree/main/Time-Related_Feature_Engineering) (11/2022)
 <details>
   <summary>Description</summary>
 I built Time-Related Feature Engineering Pipelines and developed a machine learning model with Python to predict traffic volumes.</details>
 
-### [Designing a Data Warehouse & Association Rule Mining using SQL and Python](https://github.com/pelinkeskin/Personal_projects/tree/main/DatawareHousing_AssociationRule_Mining) (10/2022)
+#### [Designing a Data Warehouse & Association Rule Mining using SQL and Python](https://github.com/pelinkeskin/Personal_projects/tree/main/DatawareHousing_AssociationRule_Mining) (10/2022)
 <details>
   <summary>Description</summary>
 This folder contains studies for practicing Data Warehousing and association rule mining.</details>
 
-### [Implementing a small Database Management System with Bash](https://github.com/pelinkeskin/Personal_projects/tree/main/DBMS_BASH) (11/2021)
+#### [Implementing a small Database Management System with Bash](https://github.com/pelinkeskin/Personal_projects/tree/main/DBMS_BASH) (11/2021)
 <details>
   <summary>Description</summary>
 I implemented a small database management system in Bash.</details>