diff --git a/README.md b/README.md index 1acafab..8b8ba30 100644 --- a/README.md +++ b/README.md @@ -2,494 +2,411 @@

DATA PROJECTS

-## iTunes Podcast Reviews Dashboards Tableau + + + + + + + + + + + +
+### iTunes Podcast Reviews Dashboards Tableau Overview: Visualization of iTunes podcast reviews using interactive dashboards. - Technologies Used: Tableau. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/41) +![Project Image](images/41.png) -Key Outcomes of the Project: Developed dashboards that enable easy exploration of user feedback trends and podcast popularity. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/41) - - ![Project Image](images/41.png) - -## Customer K-means clustering in Python + +### Customer K-means clustering in Python Overview: Clustering customer data to identify distinct groups for targeted marketing. - Technologies Used: Python, K-means clustering algorithm. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/30) +![Project Image](images/30.png) -Key Outcomes of the Project: Successfully segmented customers into clear groups based on purchasing behavior. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/30) - - ![Project Image](images/30.png) - -## Machine Learning: Decision Tree with KNIME + +### Machine Learning: Decision Tree with KNIME Overview: Using decision trees for predictive modeling in KNIME. - Technologies Used: KNIME. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/31) +![Project Image](images/31_1.png) ![Project Image](images/31_2.png) -Key Outcomes of the Project: Created a model that predicts outcomes based on historical data with high accuracy. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/31) - - ![Project Image](images/31_1.png) ![Project Image](images/31_2.png) - -## NLP Challenge: IMDB Dataset of 50K Movie Reviews to perform Sentiment Analysis +
+### NLP Challenge: IMDB Dataset of 50K Movie Reviews to perform Sentiment Analysis Overview: Analyzing a large dataset of movie reviews to determine sentiment trends using NLP techniques. - Technologies Used: Python, Natural Language Processing. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/32) +![Project Image](images/32.png) -Key Outcomes of the Project: Developed a sentiment analysis model capable of distinguishing between positive and negative reviews with high accuracy. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/32) - - ![Project Image](images/32.png) - -## Recommendation System. Collaborative Filtering + +### Recommendation System. Collaborative Filtering Overview: Building a collaborative filtering system to recommend products to users based on similar user preferences. - Technologies Used: Python, Machine Learning. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/35) +![Project Image](images/35.png) -Key Outcomes of the Project: Implemented a recommendation system that enhances user experience by personalizing product suggestions. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/35) - - ![Project Image](images/35.png) - -## Book Recommendation Model. K-Nearest Neighbors + +### Book Recommendation Model. K-Nearest Neighbors Overview: Utilizing the K-Nearest Neighbors algorithm to create a book recommendation system. - Technologies Used: Python, K-Nearest Neighbors. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/37) +![Project Image](images/37.png) -Key Outcomes of the Project: Developed a model that suggests books based on user reading patterns and preferences. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/37) - - ![Project Image](images/37.png) +
-## Amazon Customer Reviews Sentiment Analysis + + + + + + + + + + + + + + + + +
+### Amazon Customer Reviews Sentiment Analysis Overview: Performing sentiment analysis on Amazon customer reviews to gauge consumer satisfaction. - Technologies Used: Python, Natural Language Processing. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/33) +![Project Image](images/33.png) -Key Outcomes of the Project: Created insights into customer sentiments that help improve product and service quality. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/33) - - ![Project Image](images/33.png) - -## Image Classifier using TensorFlow. Keras + +### Image Classifier using TensorFlow. Keras Overview: Building an image classification model using TensorFlow and Keras. - Technologies Used: TensorFlow, Keras. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/36) +![Project Image](images/36.png) -Key Outcomes of the Project: Successfully developed a model capable of accurately classifying images into predefined categories. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/36) - - ![Project Image](images/36.png) - -## Linear Regression Health Costs Calculator + +### Linear Regression Health Costs Calculator Overview: Creating a health costs prediction model using linear regression. - Technologies Used: Python, Linear Regression. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/38) +![Project Image](images/38.png) -Key Outcomes of the Project: Developed a calculator that estimates individual health care costs based on personal data. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/38) - - ![Project Image](images/38.png) - -## Neural Network SMS Text Classifier +
+### Neural Network SMS Text Classifier Overview: Developing a text classification system using neural networks to categorize SMS messages. - Technologies Used: Python, Neural Networks. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/39) +![Project Image](images/39.png) -Key Outcomes of the Project: Created a classifier that effectively categorizes messages, enhancing communication management systems. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/39) - - ![Project Image](images/39.png) - -## Sentiment Analysis of Yelp Business Reviews + +### Sentiment Analysis of Yelp Business Reviews Overview: Analyzing Yelp reviews to extract business insights through sentiment analysis. - Technologies Used: Python, Natural Language Processing. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/15) +![Project Image](images/15.png) -Key Outcomes of the Project: Provided businesses with actionable insights into customer opinions and satisfaction levels. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/15) - - ![Project Image](images/15.png) - -## Using Streamlit for Data Visualisation + +### Using Streamlit for Data Visualisation Overview: Developing interactive data visualizations using Streamlit to enable dynamic user interactions. - Technologies Used: Streamlit, Python. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/18) +![Project Image](images/18_1.png) ![Project Image](images/18_2.png) -Key Outcomes of the Project: Created visually engaging and interactive dashboards that improve data exploration and presentation. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/18) - - ![Project Image](images/18_1.png) ![Project Image](images/18_2.png) - -## WEB scraping and Sentiment Analysis British Airways Customer Reviews +
+### WEB scraping and Sentiment Analysis British Airways Customer Reviews Overview: Extracting and analyzing sentiment from British Airways customer reviews through web scraping. - Technologies Used: Python, Web Scraping, Natural Language Processing. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/24) +![Project Image](images/24_1.png) ![Project Image](images/24_2.png) -Key Outcomes of the Project: Provided insights into customer satisfaction and service areas needing improvement. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/24) - - ![Project Image](images/24_1.png) ![Project Image](images/24_2.png) - -## Creating Dynamic Filters in Streamlit + +### Creating Dynamic Filters in Streamlit Overview: Building a Streamlit application that incorporates dynamic filters for data manipulation. - Technologies Used: Streamlit, Python. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/19) +![Project Image](images/19_1.png) ![Project Image](images/19_2.png) -Key Outcomes of the Project: Enhanced user interaction capabilities with dynamic filtering features for data sets. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/19) - - ![Project Image](images/19_1.png) ![Project Image](images/19_2.png) - -## Predicting Customer Behaviour British Airways + +### Predicting Customer Behaviour British Airways Overview: Using data analysis and machine learning to predict customer behavior for British Airways. - Technologies Used: Python, Machine Learning Algorithms. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/25) +![Project Image](images/25_1.png) ![Project Image](images/25_2.png) -Key Outcomes of the Project: Developed predictive models that assist in enhancing customer service and targeting. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/25) - - ![Project Image](images/25_1.png) ![Project Image](images/25_2.png) +
-## Kaggle Housing Prices Competition + + + + + + + + + + + + + + + + +
+### Kaggle Housing Prices Competition Overview: Participating in the Kaggle competition to predict housing prices based on various features. - Technologies Used: Python, Machine Learning, Regression Analysis. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/28) +![Project Image](images/28.png) -Key Outcomes of the Project: Achieved competitive results in predicting real estate prices. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/28) - - ![Project Image](images/28.png) - -## Kaggle Store Sales - Time Series Forecasting + +### Kaggle Store Sales - Time Series Forecasting Overview: Forecasting store sales using time series analysis in a Kaggle competition. - Technologies Used: Python, Time Series Analysis, Machine Learning. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/34) +![Project Image](images/34.png) -Key Outcomes of the Project: Developed forecasts that help in planning and optimizing store operations. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/34) - - ![Project Image](images/34.png) - -## Supervised ML: Regression Tree in Python + +### Supervised ML: Regression Tree in Python Overview: Implementing a regression tree to predict outcomes based on a set of input variables. - Technologies Used: Python, Decision Trees. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/29) +![Project Image](images/29.png) -Key Outcomes of the Project: Built a model that provides accurate predictions and insights into factor impacts. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/29) - - ![Project Image](images/29.png) - -## Machine Learning Analysis in Retail +
+### Machine Learning Analysis in Retail Overview: Analyzing retail data using machine learning to optimize inventory and sales strategies. - Technologies Used: Python, Machine Learning. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/21) +![Project Image](images/21.png) -Key Outcomes of the Project: Enhanced decision-making processes with predictive analytics in retail management. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/21) - - ![Project Image](images/21.png) - -## Credit Card Fraud Detection using Scikit-Learn and Snap ML + +### Credit Card Fraud Detection using Scikit-Learn and Snap ML Overview: Developing a model to detect fraudulent transactions using machine learning. - Technologies Used: Python, Scikit-Learn, Snap ML. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/22) +![Project Image](images/22_1.png) ![Project Image](images/22_2.png) -Key Outcomes of the Project: Increased security by accurately identifying potentially fraudulent activities. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/22) - - ![Project Image](images/22_1.png) ![Project Image](images/22_2.png) - -## Natural Language Processing with Hugging Face Transformers + +### Natural Language Processing with Hugging Face Transformers Overview: Leveraging Hugging Face Transformers for advanced natural language processing tasks. - Technologies Used: Python, Hugging Face Transformers. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/23) +![Project Image](images/23_1.png) ![Project Image](images/23_2.png) -Key Outcomes of the Project: Developed capabilities for complex language understanding and generation tasks. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/23) - - ![Project Image](images/23_1.png) ![Project Image](images/23_2.png) - -## Auto Exploratory Data Analysis with D-Tale, SweetViz, Pandas Profiling +
+### Auto Exploratory Data Analysis with D-Tale, SweetViz, Pandas Profiling Overview: Automating the exploratory data analysis process using various Python libraries. - Technologies Used: Python, D-Tale, SweetViz, Pandas Profiling. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/26) +![Project Image](images/26.png) -Key Outcomes of the Project: Streamlined data analysis workflows, providing quick and comprehensive data insights. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/26) - - ![Project Image](images/26.png) - -## Auto ML and Bespoke ML with sklearn (Random Forest, Logistic Regression, SVC) + +### Auto ML and Bespoke ML with sklearn (Random Forest, Logistic Regression, SVC) Overview: Implementing both automated and custom machine learning solutions using Scikit-Learn. - Technologies Used: Python, Scikit-Learn. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/27) +![Project Image](images/27.png) -Key Outcomes of the Project: Developed versatile models for classification and regression tasks with improved accuracy. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/27) - - ![Project Image](images/27.png) - -## Assess the Quality of a Dataset for a Public Service Agency + +### Assess the Quality of a Dataset for a Public Service Agency Overview: Evaluating and improving the quality of a dataset used by a public service agency. - Technologies Used: Data Quality Assessment. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/4) +![Project Image](images/4.png) -Key Outcomes of the Project: Enhanced data reliability and usability for critical public service applications. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/4) - - ![Project Image](images/4.png) +
-## Data Transformation Pipeline with Cloud Dataprep (Alteryx) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+### Data Transformation Pipeline with Cloud Dataprep (Alteryx) Overview: Designing and implementing a data transformation pipeline using Cloud Dataprep similar to Alteryx. - Technologies Used: Cloud Dataprep, Alteryx. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/40) +![Project Image](images/40.png) -Key Outcomes of the Project: Optimized data processing workflows, enabling more efficient data analytics. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/40) - - ![Project Image](images/40.png) - -## Correlation in Python + +### Correlation in Python Overview: Exploring statistical correlations within datasets using Python. - Technologies Used: Python, Statistical Analysis. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/20) +![Project Image](images/20.png) -Key Outcomes of the Project: Identified key relationships between variables, supporting informed decision-making. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/20) - - ![Project Image](images/20.png) - -## Explore Data Using SQL in Google Colab + +### Explore Data Using SQL in Google Colab Overview: Conducting data exploration and analysis using SQL within the Google Colab environment. - Technologies Used: SQL, Google Colab. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/17) +![Project Image](images/17.png) -Key Outcomes of the Project: Enabled efficient data querying and manipulation directly within a notebook environment. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/17) - - ![Project Image](images/17.png) - -## SQL Sub-queries in Google Colab +
+### SQL Sub-queries in Google Colab Overview: Demonstrating the use of SQL sub-queries for complex data queries in Google Colab. - Technologies Used: SQL, Google Colab. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/16) +![Project Image](images/16.png) -Key Outcomes of the Project: Enhanced data analysis capabilities with advanced SQL techniques. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/16) - - ![Project Image](images/16.png) - -## Create a Dashboard Meeting Business Requirements + +### Create a Dashboard Meeting Business Requirements Overview: Developing a customized dashboard to meet specific business analysis needs. - Technologies Used: Dashboard Design, Business Analysis. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/6) +![Project Image](images/6.png) -Key Outcomes of the Project: Delivered a tailored dashboard that supports strategic business decisions. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/6) - - ![Project Image](images/6.png) - -## Retrieve User Activity Data on an Online Forum Using SQL + +### Retrieve User Activity Data on an Online Forum Using SQL Overview: Extracting and analyzing user activity data from an online forum using SQL. - Technologies Used: SQL, Data Analysis. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/7) +![Project Image](images/7.png) -Key Outcomes of the Project: Gained insights into user engagement and behavior patterns. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/7) - - ![Project Image](images/7.png) - -## Working with Web APIs and JSON on Movies Dataset +
+### Working with Web APIs and JSON on Movies Dataset Overview: Utilizing web APIs to fetch and process movie data stored in JSON format. - Technologies Used: Web APIs, JSON, Python. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/2) +![Project Image](images/2.png) -Key Outcomes of the Project: Efficiently retrieved and manipulated movie data for analysis and application development. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/2) - - ![Project Image](images/2.png) - -## Explore a Dataset on Energy Usage and Draw First Conclusions + +### Explore a Dataset on Energy Usage and Draw First Conclusions Overview: Analyzing an energy usage dataset to uncover patterns and draw initial conclusions. - Technologies Used: Data Analysis, Visualization Techniques. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/5) +![Project Image](images/5.png) -Key Outcomes of the Project: Identified significant factors affecting energy consumption, aiding in energy conservation efforts. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/5) - - ![Project Image](images/5.png) - -## Create a Web Server and an Amazon RDS DB Instance + +### Create a Web Server and an Amazon RDS DB Instance Overview: Setting up a web server connected to an Amazon RDS database for handling dynamic web applications. - Technologies Used: Web Server Management, Amazon RDS. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/3) +![Project Image](images/3.png) -Key Outcomes of the Project: Established a robust backend infrastructure for scalable web applications. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/3) - - ![Project Image](images/3.png) - -## Data Analysis using Pandas and SQLite3 +
+### Data Analysis using Pandas and SQLite3 Overview: Conducting comprehensive data analysis using Pandas in conjunction with SQLite3 for database management. - Technologies Used: Pandas, SQLite3, Python. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/14) +![Project Image](images/14.png) -Key Outcomes of the Project: Streamlined data analysis processes and improved data accessibility and manipulation. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/14) - - ![Project Image](images/14.png) - -## E-commerce Store Sales Analysis + +### E-commerce Store Sales Analysis Overview: Analyzing sales data from an e-commerce platform to optimize marketing and sales strategies. - Technologies Used: Data Analysis, Business Intelligence. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/8) +![Project Image](images/8.png) -Key Outcomes of the Project: Enhanced understanding of customer purchasing patterns and sales performance. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/8) - - ![Project Image](images/8.png) - -## Exploratory Data Analysis on Diamonds Dataset + +### Exploratory Data Analysis on Diamonds Dataset Overview: Performing exploratory data analysis on a dataset of diamonds to understand pricing factors. - Technologies Used: Data Visualization, Statistical Analysis. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/9) +![Project Image](images/9.png) -Key Outcomes of the Project: Identified key price drivers and anomalies in the dataset. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/9) - - ![Project Image](images/9.png) - -## Data Cleaning, Transformation, and Visualisation on AirBnB London Dataset +
+### Data Cleaning, Transformation, and Visualisation on AirBnB London Dataset Overview: Cleaning, transforming, and visualizing data from the AirBnB London dataset to derive actionable insights. - Technologies Used: Data Cleaning, Data Transformation, Data Visualization. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/12) +![Project Image](images/12.png) -Key Outcomes of the Project: Improved data quality and provided clear visual insights into the Airbnb market in London. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/12) - - ![Project Image](images/12.png) - -## Data Cleaning on Movies Dataset + +### Data Cleaning on Movies Dataset Overview: Performing data cleaning on a comprehensive movies dataset to prepare for further analysis. - Technologies Used: Data Cleaning, Python. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/10) +![Project Image](images/10.png) -Key Outcomes of the Project: Prepared a clean and structured dataset for accurate and efficient data analysis. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/10) - - ![Project Image](images/10.png) - -## Short-Term Rental Analytics on AirBnB Bristol Dataset + +### Short-Term Rental Analytics on AirBnB Bristol Dataset Overview: Analyzing short-term rental data from Airbnb in Bristol to understand market trends and rental dynamics. - Technologies Used: Data Analysis, Business Intelligence. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/11) +![Project Image](images/11.png) -Key Outcomes of the Project: Delivered insights on pricing strategies and occupancy rates to maximize rental income. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/11) - - ![Project Image](images/11.png) - -## Data Cleaning, Merging, Transforming on Movies Dataset +
+### Data Cleaning, Merging, Transforming on Movies Dataset Overview: Enhancing a movies dataset by cleaning, merging, and transforming data to support detailed analysis. - Technologies Used: Data Cleaning, Data Merging, Data Transformation. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/13) +![Project Image](images/13.png) -Key Outcomes of the Project: Created a comprehensive and enriched dataset that facilitates advanced film industry analysis. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/13) - - ![Project Image](images/13.png) - -## Exploratory Data Analysis on Movies Dataset + +### Exploratory Data Analysis on Movies Dataset Overview: Conducting exploratory data analysis on a movies dataset to uncover trends and insights. - Technologies Used: Data Analysis, Visualization. +[View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/1) +![Project Image](images/1.png) -Key Outcomes of the Project: Provided deep insights into industry trends, popularity metrics, and financial aspects of films. - -Link to the Project Repository: [View Project's Files](https://github.com/sitshayeva/portfolio/tree/main/projects/1) - - ![Project Image](images/1.png) + +