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mohdrahidkhan

Mohd Rahid Khan

@mohdrahidkhan

Data Scientist, ML, Python and Streamlit Developer

Inde
Anglais, Ourdou, Hindi
Certaines informations sont présentées en anglais.
À propos de moi
I am a Data Science enthusiast and Python developer specializing in data analysis, ml, predictive analytics, and interactive Streamlit applications. I can help with data cleaning and preprocessing, EDA, SQL analysis, data visualization, machine learning models, predictive analytics, customer segmentation, and interactive Streamlit dashboards. I work with Python, SQL, Pandas, NumPy, Scikit-learn, Matplotlib, Plotly, and Streamlit to deliver practical, reliable, and user-friendly solutions. I focus on understanding client requirements and delivering clear, reliable, and user-friendly solutions.... Plus d’infos

Compétences

m
mohdrahidkhan
Mohd Rahid Khan
hors ligne • 
Temps de réponse moyen de 1 heure

Voir mes services

Nettoyage des données
I will clean, preprocess, and prepare your data using python
De15 $US / projetPlus de détails
Modélisation et analyse statistique
I will perform data analysis and exploratory data analysis using python
De15 $US / projetPlus de détails

Portfolio

Expérience professionnelle

UniConverge_Technologies

Data Science Intern

UniConverge Technologies

Jun 2026 - Jul 2026 • 1 mo

During my 6-week Data Science internship at UniConverge Technologies, I worked on a customer segmentation project using data analysis and machine learning techniques. I performed data analysis and preprocessing to understand customer purchasing behavior and identify meaningful patterns. I conducted RFM (Recency, Frequency, and Monetary) analysis to evaluate customer engagement and purchasing activity. Using K-Means clustering, I identified meaningful customer segments based on customer behavior and analyzed their characteristics to generate actionable business insights. I also developed an interactive Streamlit dashboard to present customer segments, visualizations, and business insights in a clear and user-friendly way. The project involved working with Python, Pandas, NumPy, Scikit-learn, and data visualization techniques. Key areas of work included: • Data Cleaning and Preprocessing • Exploratory Data Analysis (EDA) • RFM Analysis • Customer Segmentation • K-Means Clustering • Machine Learning • Data Visualization • Interactive Dashboard Development This internship provided hands-on experience in applying Data Science and Machine Learning techniques to a real-world business problem and transforming raw data into meaningful insights.