Freelancers using Streamlit in ThessalonikiFreelancers using Streamlit in Thessaloniki

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AI Integration & Automation Engineer | Full-Stack Web Apps
$50k+
Earned
63x
Hired
4.9
Rating
97
Followers
AI Integration & Automation Engineer | Full-Stack Web Apps
Power BI Data Analyst + ML AI Automation Expert
5.0
Rating
96
Followers
Power BI Data Analyst + ML AI Automation Expert
Cover image for Predictive Marketing Analytics: Optimizing Advertising
Predictive Marketing Analytics: Optimizing Advertising ROI 1. The Business Problem Companies often struggle to determine which marketing channels actually drive revenue. Without a data-driven approach, advertising budgets are often misallocated across platforms like TV, Radio, and Newspapers, leading to inefficient spending and missed sales targets. This project aimed to build a predictive model to quantify the relationship between multi-channel marketing spend and total sales. 2. Strategic Insights & Market Analysis Through a rigorous analysis of historical advertising data, I identified the specific drivers of revenue growth: Dominant Revenue Driver: TV advertising emerged as the most critical factor, showing a massive 0.9 correlation with sales. Efficiency Analysis: While Radio and Newspaper spending contributed to the marketing mix, their direct impact on sales was significantly lower (0.35 and 0.16 correlation, respectively), suggesting a need for budget reallocation. Predictive Power: My analysis revealed that 81.6% of the variance in sales can be explained by TV advertising spend alone, providing a highly reliable foundation for future budget forecasting. 3. Data-Driven Solution I developed a Linear Regression model to provide leadership with a mathematical framework for sales forecasting. Reliability: The model was validated using a 70/30 train-test split, ensuring it performs accurately on new, unseen market data. Accuracy: The system achieved a strong R-squared value of 0.79 on the test set, meaning it can accurately predict nearly 80% of sales fluctuations based on planned marketing spend. Error Management: I performed a detailed residual analysis to confirm that the model’s error terms were normally distributed, ensuring the reliability of the forecasted figures. 4. Business Impact Budget Optimization: Provided a clear mathematical equation (Sales=6.948+0.054Γ—TV) that allows the marketing team to calculate the expected return on every dollar spent on TV advertising. Strategic Planning: Enabled the transition from "gut-feeling" marketing to precision budgeting, allowing the company to maximize ROI by prioritizing high-impact channels. Risk Mitigation: By identifying the variance that the model couldn't explain, I helped the business identify where external market factors might still influence sales, allowing for more conservative and realistic financial planning. Technical Stack Modeling: Simple Linear Regression, Statsmodels (OLS), Scikit-learn. Analytics: Python, Pandas, NumPy. Visualization: Seaborn, Matplotlib, 3D Scatter Plots
4
5
754
Cover image for Sentiment-Driven E-commerce Optimization: Amazon Review
Sentiment-Driven E-commerce Optimization: Amazon Review Analysis & Rating Prediction. Project Overview βœ… This project serves as a machine learning proof of concept designed to transform raw Amazon product reviews into actionable business insights. By automating the prediction of review ratings and analyzing customer sentiment, the system enables brands to optimize product listings, proactively address customer pain points, and drive higher conversion rates. Process βœ… I developed an end-to-end pipeline covering data acquisition, complex text processing, and model deployment. Automated Data Scraping, Integrated the Apify API to extract real-time customer feedback directly from Amazon product URLs. I configured the scraper to handle up to 100 reviews per run, capturing critical metadata including rating scores, review descriptions, and verified purchase status. Data Refinement & Feature Engineering: Cleaned a dataset of approximately 1,944 reviews by removing noise (punctuation/symbols) and stop words using NLTK. I implemented TF-IDF Vectorization to convert text into numerical features and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, ensuring the model could accurately predict rare negative reviews. Model Benchmarking βœ… Developed and compared three distinct architectures to identify the most robust predictor: Naive Bayes: High-speed probabilistic classification. Support Vector Classifier (SVC): Optimized for high-dimensional text data. Neural Network (MLPClassifier): To capture complex semantic patterns. Web App Deployment: Built a dedicated Streamlit dashboard that allows non-technical stakeholders to input raw review text and receive instant rating predictions with confidence scores. Technical Stackβœ… Languages & Tools: Python, Apify Client. ML & NLP Libraries: Scikit-learn (SVC, Naive Bayes, MLP), NLTK (Tokenization, Stopwords), Imbalanced-learn (SMOTE). Deployment: Streamlit, Joblib (Model Serialization). Visualization: Plotly, WordCloud, Matplotlib. Key Results βœ… Achieved a peak accuracy of 95.27% using the Neural Network model, with the SVC model following closely at 94.46%. Developed sentiment-based feedback loops within the app: high ratings (4-5 stars) trigger positive marketing recommendations, while low ratings (1-2 stars) alert teams to address product issues like battery life or build quality. Enabled real-time competitive analysis by providing a user-friendly interface for cross-functional marketing and product development teams to audit customer sentiment at scale.
2
5
795
Marketing Data Specialist bridging marketing & data science
Marketing Data Specialist bridging marketing & data science
Freelance Data Scientist | Python & ML Expert
10
Followers
Freelance Data Scientist | Python & ML Expert
Senior FullStack & AI Engineer | Product-Driven Solutions
$25k+
Earned
5x
Hired
5.0
Rating
18
Followers
Senior FullStack & AI Engineer | Product-Driven Solutions
Full Stack Dev | MVP Developer | React Node Next.js
$10k+
Earned
4x
Hired
5.0
Rating
50
Followers
Full Stack Dev | MVP Developer | React Node Next.js
AI Video & Automation Architect | n8n + ComfyUI
3x
Hired
5.0
Rating
51
Followers
AI Video & Automation Architect | n8n + ComfyUI
Cover image for πŸš€ GrabClip v3.0 is officially
πŸš€ GrabClip v3.0 is officially live! We haven't stopped building. We just completely redesigned the batch downloading experience to make grabbing your media faster, smarter, and more automated. Whether you need to download a massive 4+ hour video, pull an entire educational "Learning Path" playlist, or grab multiple links from over 1000+ different websites (YouTube, TikTok, Instagram, X, etc.) at the same timeβ€”GrabClip v3.0 handles it effortlessly. ✨ What’s new in v3.0? ⚑ Completely Redesigned Batch Queue: The batch system has been rebuilt from the ground up into a clean, dedicated popup window. Paste your URLs, and GrabClip instantly auto-detects the platform for each one. Pick your format (4K, 1080p, MP3, FLAC), hit "Download All", and track everything with real-time progress bars for individual items and the overall batch. πŸš€ Speed Boost Built-In: No more digging through settings. The 1x to 8x Speed Boost slider is now built directly into the batch dialog! Crank it up to 8x, hit download, and watch heavy 4K files and long learning paths fly through. πŸ€– Smarter Madame AI Automation: Our smart assistant is now deeply wired into your workflow. Just copy multiple URLs to your clipboard, and the batch dialog opens automatically. Madame AI tracks the progress and notifies you at every single stageβ€”from analysis to download completion. πŸ“š Ultimate Stability & Smarter History: Everything you loved from v2.0 is still here and improved. Enjoy a smarter download history with custom sorting, zero duplicates, a new stats bar, and crucial bug fixes for an incredibly smooth experience. ━━━━━━━━━━━━━━━━━━ πŸ”‘ As always, GrabClip is 100% FREE. Get your License Key and download the v3.0 update here: πŸ‘‰ grabclip.bilsimaging.com (http://grabclip.bilsimaging.com) ━━━━━━━━━━━━━━━━━━ We automate. We build. We create. β€” Bilsimaging, Tunisia πŸ‡ΉπŸ‡³
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