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Project details
My project represents a culmination of intensive efforts aimed at pushing the boundaries of predictive modeling. Beyond applying standard techniques, I've delved deep into data preprocessing, employing cutting-edge methodologies to cleanse and prepare the data. My innovative approach to feature engineering extracts nuanced insights, enriching model performance.
What distinguishes my work is meticulous attention to addressing data quality issues and outliers. Through rigorous outlier detection and robust transformation techniques, I've ensured model resilience, enhancing predictive accuracy and reliability.
Additionally, analyzing feature importances has unearthed invaluable insights into underlying dynamics. This not only deepens understanding but also guides future model refinement and strategic decisions.
In essence, my project showcases a holistic approach, delivering accurate predictions and empowering stakeholders with actionable insights.
What distinguishes my work is meticulous attention to addressing data quality issues and outliers. Through rigorous outlier detection and robust transformation techniques, I've ensured model resilience, enhancing predictive accuracy and reliability.
Additionally, analyzing feature importances has unearthed invaluable insights into underlying dynamics. This not only deepens understanding but also guides future model refinement and strategic decisions.
In essence, my project showcases a holistic approach, delivering accurate predictions and empowering stakeholders with actionable insights.
Machine Learning Tools
Keras, Microsoft Excel, NumPy, pandas, Python, R, SAS, scikit-learn, TensorFlow, XGBoostWhat's included $70
These options are included with the project scope.
$70
- Delivery Time 15 days
- Number of Revisions Unlimited
- Number of Model Variations 10
- Number of Scenarios 10
- Number of Graphs/Charts 15
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 7 Days Delivery
+$20
Additional Graph/Chart
+$10
6 reviews
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FG
Fidel G.
Aug 1, 2024
R Script and Excel data table to compare data from two different digital health platforms
SM
Seif M.
Jul 31, 2024
VAR
Tengizi's work is beyond exceptional and I can't recommend him enough! This is my second time working with him, and he's fully dedicated, he brings exceptional work and goes above and beyond to deliver quality work in a short time. If you need an econometrician, definitely go for Tengizi!
SM
Seif M.
Apr 2, 2024
R/Coding - AIC test and regression formation
KB
Karan B.
Mar 16, 2024
R Project
MI
Misha I.
Feb 12, 2024
Created Visualizations for Sales Calls Statistics to estimate Sales Reps work intensity & peak hours
My second project with Tengizi. Super fast & fantastic quality of execution. Initially, we aimed at doing some simpl(er) Excel charts but in the end Tengizi provided very detailed & nicely made data visualization from way more sophisticated tools.
Looking forward to our next collaboration!
Looking forward to our next collaboration!
About Tengizi
Econometrician | Data Scientist | Data Analyst | R & Python Specialist
100%
Job Success
Bratislava, Slovakia - 10:19 am local time
Working with me will guarantee you: ⬇️
✅𝐇𝐢𝐠𝐡-𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐰𝐨𝐫𝐤
✅𝐅𝐚𝐬𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲
✅𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲
✅𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧
✅𝐂𝐮𝐬𝐭𝐨𝐦 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬
✅𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞 𝐢𝐧 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧
🟢**𝐄𝐜𝐨𝐧𝐨𝐦𝐞𝐭𝐫𝐢𝐜 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐏𝐫𝐨𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲**🟢
I possess a strong foundation in econometric modeling, encompassing both cross-sectional and time-series data analysis. This proficiency allows me to formulate, estimate, and interpret econometric models that capture complex relationships between economic variables.
🟢**𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬**🟢
My skill set includes adeptness in data analysis techniques, allowing me to extract meaningful insights from complex datasets efficiently. I'm experienced in exploratory data analysis, statistical modeling, and hypothesis testing.
🟢**𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞**🟢
I have a deep understanding of machine learning algorithms and techniques, ranging from supervised and unsupervised learning to deep learning. This expertise enables me to develop predictive models, classify data, and uncover patterns in large datasets.
🟢**𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐒𝐤𝐢𝐥𝐥𝐟𝐮𝐥𝐧𝐞𝐬𝐬**🟢
Proficient in data science methodologies, I excel in data preprocessing, feature engineering, and model evaluation. Additionally, I am experienced in deploying machine learning models to solve real-world problems and optimizing their performance for maximum accuracy and efficiency.
I aim to leverage my expertise in econometric modeling alongside machine and deep learning techniques to enhance research and decision-making in diverse domains. Proficient in statistical analysis, predictive modeling, and econometric methodologies, my goal is to drive innovation and problem-solving across various fields. With a versatile skill set, I am equipped to develop practical solutions spanning econometrics and beyond.
In conclusion, my expertise in econometrics, machine learning, and deep learning, combined with my strong programming skills in Python and R, positions me to make valuable contributions to a wide array of research and decision-making endeavors. My dedication to applying these tools to diverse fields reflects my commitment to driving innovation and making a meaningful impact. I am enthusiastic about exploring opportunities for collaboration within the exciting space where econometrics and AI converge, and I look forward to contributing to the advancement of knowledge and solutions in this area.
Steps for completing your project
After purchasing the project, send requirements so Tengizi can start the project.
Delivery time starts when Tengizi receives requirements from you.
Tengizi works on your project following the steps below.
Revisions may occur after the delivery date.
Importing Libraries and Dataset
Begin by loading necessary Python libraries (like pandas, numpy, seaborn, matplotlib) and the dataset for analysis.
Data Cleaning
Involves removing duplicate records and handling missing values to ensure data quality. Techniques such as filling missing values with the mean or median, or dropping them, may be used.