OCHA Centre for Humanitarian Data’s Post

As a data science intern at Encryptix, I have gained many information and knowledge about the concepts behind building several models using machine learning, to predict the models and the execution of the models. These tasks or projects will help in upgrading my resume and this experience will really help me in my data science journey.

Over the past few weeks, I've had the incredible opportunity to work on diverse and challenging projects that have significantly boosted my skills as a Data science intern. Here's a glimpse into what I've accomplished: 🔹Iris Flower Classification:The Iris flower classification involves predicting the species of iris flowers (setosa, versicolor, virginica) based on four features: sepal length, sepal width, petal length, and petal width. The dataset contains 150 samples and is a well-known benchmark for pattern recognition and machine learning algorithms. 🔹Credit Card Fraud Detection: I developed and fine-tuned models to identify fraudulent transactions by analyzing patterns in transaction data. This role enhanced my skills in data preprocessing, feature engineering, and implementing anomaly detection algorithms to improve the system's ability to detect and prevent fraud. 🔹 Movie Rating Prediction: Movie rating prediction involves developing algorithms to predict user ratings for movies based on historical rating data. This typically uses collaborative filtering, content-based filtering, or hybrid methods to analyze user preferences and movie features, aiming to provide personalized recommendations. The task is a key component in recommendation systems used by platforms like Netflix and Amazon. Big thanks to Encryptix for this incredible learning opportunity! hashtag #datascience #encryptix #internshipexperience

DATA ANALYST INTERNSHIP AT Quantum Analytics NG Hello everyone, I am here again with my 8th project from Quantum Analytics. Title: Shark Attack Data Tools: Microsoft Power BI Data Source: Quantum Analytics NG This shark attack data includes information about incidents where humans were attacked by sharks. This data covers aspect such as the species of the shark, the severity of the attack (fatal or not fatal), details about the victim (gender, activity of that time of the attack). It also analyzed the country with higher incidences of shark attacks. Appreciation to Quantum Analytics NG, Jonathan Osagie and our esteemed tutors for their invaluable guidance and support throughout this journey. Stay tuned for my next project 8/13. #dataanalytics #dataanalyst #powerbi #dashboard #datapresentation #project

🚀I’m excited to share that I have successfully completed my second task as a Data Science Intern at Prodigy InfoTech. This experience allowed me to deepen my understanding of data preprocessing and exploratory data analysis (EDA), uncovering meaningful patterns and relationships within the dataset. 📊🔍I'm eager to apply these insights in future projects! #ProdigyInfoTech #DataScience

 Excited to share my experience with Task 02 of the Prodigy InfoTech Data Science Internship! ### Task Overview: In Task 02, I focused on developing insightful visualizations using Python libraries such as Matplotlib and Seaborn. By exploring a heart disease dataset, I analyzed both categorical and continuous data distributions, highlighting key factors affecting heart health. ### Dataset: -   The heart disease dataset was central to this task, offering extensive data for in-depth analysis. ### How to Explore: 1.   Clone the repository to your local machine: [Repository Link](https://lnkd.in/d4trYC_Y) 2.   Ensure you have Python and the required libraries installed. 3.   Execute `PRODIGY_DS_02.ipynb` to generate and view the visualizations. ### Repository Structure: - `heart.csv`: The dataset used for analysis. - `PRODIGY_DS_02.ipynb`: Jupyter Notebook for data processing and visualization. - `README.md`: A guide to navigating the repository and running the scripts. ### Key Insights: 1. **Health Status Distribution**:   - 138 healthy individuals and 165 individuals with heart disease were identified.   - Visualized using a bar chart to show the distribution of health status. 2. **Gender Analysis**:   - The dataset contains more male records than female.   - Males were found to be comparatively healthier than females. 3. **Chest Pain**:   - Individuals with a chest pain level of 2 are more likely to be unhealthy.   - Visualized using a count plot. 4. **Blood Pressure**:   - High blood pressure shows significant variation between healthy and unhealthy individuals. 5. **Correlation Heatmap**:   - A heatmap was used to show correlations between numerical features, highlighting significant relationships affecting heart health. 6. **Other Factors**:   - Cholesterol, maximum heart rate (thalach), number of vessels colored by fluoroscopy, oldpeak (ST depression), and slope of the peak exercise ST segment were identified as key indicators of heart health. ### Visualizations: - Histograms, box plots, and line plots were used to explore distributions and trends. - Count plots and bar charts were employed to compare categorical data. This internship task has been an incredible learning experience, allowing me to apply data visualization techniques to a real-world medical dataset.   Each visualization provides a clear view of the factors influencing heart health. Ready to explore further? Let's connect and discuss the insights I've uncovered!   #DataScience #DataVisualization #Python #InternshipExperience #ProdigyInfoTech

I am excited to present task 2 of Prodigy InfoTech data science internship . ⭕project link:https://lnkd.in/ggr69_nP The details are a data analysis and machine learning workflow applied to the Titanic dataset. Key analyses include the examination of survival rates based on gender, age, passenger class, and other variables, leading to logistic regression and random forest modeling to predict survival outcomes. The final results indicate a test accuracy of approximately 87%, showcasing the model's effectiveness in capturing the patterns of survival from the given data. #datascience #ProdigyInfoTech #Titanic #machine_learning #Data_Visualization

Students in core engineering departments who have a vague idea about becoming a data scientist are losing out by not concentrating on core domain subjects. Instead, if they develop their experience as a domain+AI/ML engineer, they could build a unique career with far less competition, while being a data scientist. Developing irreplaceable skills is the best hedge against a bearish job market. Maybe some of the blame is also on Professors that we haven’t been able to convince students about the importance of developing core knowledge of CE/ME/EE etc. But we live in the era of hyperspecialization and the evolving job market is already casting a long shadow on campus placement stats everywhere.

Exciting News! 🎉 During my Data Science Internship journey at Innomatics Research Labs, I had an incredible opportunity to dive deep into the world of data science by performing a capstone EDA project 📈 on AMCAT dataset. This dataset holds information about an information about candidate's skills, academic background, and test scores. During this EDA project 📊, I learned more about complex datasets, different Python libraries, patterns, and trends. Also, I have learned more about visualization through different graphs such as histograms, heatmaps, box plots, scatter plots, and bar charts. I Explored various visualization techniques to gain an understanding of the data at hand. Moreover, I performed statistical non-visual analysis from the dataset about the salary of candidates. I am grateful to Innomatics Innomatics Research Labs for providing me with a valuable opportunity to work more on my skills and make meaningful insights into the field of data Analysis. Special thanks to my Mentor Kanav Bansal sir😊. Github Link: https: https://lnkd.in/gRn9epJf hashtag#exploratorydataanalysis hashtag#pyhton hashtag#dataanalytics  hashtag#datavisualization hashtag#statistics hashtag#internshipexperience  hashtag#innomaticsresearchlabs hashtag#innomotics

Future of Construction Industry: The fusion of #Civil/#Construction #Engineering with #AI/#ML Skills paves the way for #Data #Scientist Practitioners in the Construction industry. It's a fresh perspective on enhancing core domain skills. Let's dive in and prepare for the #Challenging #transformation! #civilengineering #constructionmanagement #AI #ML #Datascientist