Python Classification Model from Excel

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@helgekrueger

Hello. I can build a jupyter notebook that reads your excel file and first performs standard analysis steps: - For each row containing numerical values, compute statistics, e.g. standard deviation, value counts, plot histograms. It will probably be a selection depending on what the values look like. - Perform some level of rough normalization, e.g. z-score + outlier detection. - Tests if standard classification models apply. Classification models will come from python scikit learn, e.g. support vector machine The main output metric will be accuracy. I will see how well the others can be computed.

@AhmadXpert2410

Hi there, I’m a Data Scientist / AI Engineer with hands-on experience building classification and predictive ML pipelines using Python, Pandas, NumPy, scikit-learn, XGBoost, and Random Forest. I can take your Excel data from raw rows through cleaning, exploratory checks, feature engineering, training, validation, and final evaluation. I’ll build a clean, reproducible Jupyter Notebook or Python script that handles missing values, duplicates, data types, categorical features, scaling where required, train/test splitting, and appropriate validation. I can compare suitable classification algorithms and use cross-validation and hyperparameter tuning where it provides meaningful improvement, while avoiding data leakage. The final workflow will include accuracy, precision, recall, F1, confusion matrix, and other relevant metrics, along with visualizations to make the model behavior easy to understand. I’ll also provide a serialized joblib/pickle model and clear instructions for loading it and generating predictions on new Excel data. Regards, Ahmad

@syphaxzen

Hi, I can build the complete machine learning workflow for your Excel dataset, from loading and cleaning the data to preprocessing, feature engineering, model selection, training, validation, and evaluation. I’ll choose the most suitable tools and approach based on the actual dataset instead of being limited to specific libraries. The code will be clean, well-commented, and easy to run again on your machine. I’ll also include useful performance metrics and visual checks to help you understand how well the model performs. If needed, I can add hyperparameter tuning, improve the feature set, save the trained model, and provide a short README or inline documentation explaining how to use it with new data. Some details about the Excel files, target column, and classification requirements are missing, so we can discuss them in chat before starting and decide on the best approach. I can start right away.

@innovateNsolve

Hello, I am a PhD in Computer Science with 30+ years of experience in research, academics, and professional AI/ML development. I have extensive experience developing and evaluating machine-learning and deep-learning models using Python, including data preprocessing, feature engineering, model selection, validation, hyperparameter tuning, and performance analysis. I can develop a complete, reproducible classification workflow for your Excel dataset. I will structure the solution as a reusable pipeline so that you can load fresh Excel data and generate predictions without rebuilding the workflow manually. Looking forward for a collaboration. Thanks/

@Web1devloper

With my extensive experience in Data Analytics and Science, I am confident I can deliver a high-quality solution for your project. As an expert in Python, Pandas, and NumPy, with a profound understanding of optimizing operations and forecasting outcomes, building a classification model from Excel data is second nature to me. I have done this exact task numerous times before, utilizing robust libraries such as scikit-learn for training models and matplotlib/seaborn for visualizations. Moreover, my eight-plus years of focused work in the data industry have honed my ability to transform complex raw data into valuable business insights - precisely what you need! Whether it is cleaning and preparing the data or feature engineering and hyper parameter tuning to improve model performance, I will ensure each step of the process is #done right. I guarantee accuracy, precision-recall metrics, and even generating an efficient confusion matrix or similar evaluation report.

@devanshkhandekar

Hi,I’m an Applied ML/AI Engineer with hands-on experience building reproducible classical ML,deep-learning,classification,& time-series pipelines for real-world structured datasets -I’ve developed high-dimensional classification systems using Logistic Regression,LDA,Random Forest/ExtraTrees & gradient boosting,with hardened train/test splits,leakage checks,calibration,class-wise metrics & reproducible model evaluation -I’ve also built time-series forecasting pipelines with lag/rolling features,residual modelling,gradient boosting,normalization controls & hidden-test validation—patterns directly applicable to financial risk/transaction modelling & retail demand/sales forecasting. -Approach: first profile the Excel sheets for missing values,duplicates,class imbalance,categorical fields,outliers & potential target leakage -Build a reusable `ColumnTransformer` pipeline for imputation,encoding & scaling,then benchmark Logistic Regression,Random Forest,XGBoost/LightGBM & MLP models -For imbalanced targets,I’ll compare class weighting/SMOTE using stratified cross-validation rather than relying only on accuracy -Evaluation will include precision,recall,F1,ROC-AUC/PR-AUC,confusion matrix,calibration & feature importance/SHAP -Hyperparameter tuning will use RandomizedSearch/Optuna with validation isolated from the final test set -Deliverable:clean Jupyter/Python workflow,plots,feature report,requirements file & README showing how to score new Excel data with one command

@UzeeTech

Your Excel data can become a validated, reusable classification model quickly when the pipeline is built for repeatability from the start — that is the kind of compact, practical ML work I enjoy. My approach: load the sheets with pandas, clean missing values and types, then wrap preprocessing and modeling in a scikit-learn pipeline. I would benchmark logistic regression, random forest and gradient boosting, tune the best with cross-validation, and report accuracy, precision-recall and the confusion matrix. Deliverables: a clean Jupyter notebook with markdown notes, a short readme, and the saved model file for reuse. I recently built Jyotish AI, a live AI astrology app on Android and web, with Python data pipelines and ML turning raw inputs into reliable predictions — the same discipline of clean data in, trustworthy outputs out. Roughly how many rows and columns are in the sheets, and is the target binary or multi-class? Happy to start with a quick look at a sample whenever you're ready.

@mcollinscalz

Hi, my name is Collins and I build production-ready classification pipelines in Python and specialize in turning messy Excel spreadsheets into high-performing, reproducible scikit-learn models. My Step-by-Step Approach: Data Preparation: Ingest Excel sheets using pandas, handle missing values, encode categorical variables, and remove outliers. Feature Engineering & Preprocessing: Scale numerical features, create domain-specific features if applicable, and construct a modular scikit-learn Pipeline to prevent data leakage. Model Selection & Tuning: Train baseline models (Random Forest, XGBoost, or Logistic Regression) and optimize hyper-parameters using GridSearchCV/RandomizedSearchCV. Evaluation & Visualization: Generate clean seaborn performance plots including confusion matrices, ROC-AUC curves, and detailed precision-recall classification reports. Deliverables Included: Clean, well-commented Jupyter Notebook (.ipynb) and standalone script (.py) engineered to run smoothly on your local machine. Serialized, ready-to-use model file (.joblib or .pkl). Comprehensive Markdown README detailing setup instructions, pipeline breakdown, and instructions for scoring fresh data. I can deliver the complete, runnable workflow within 24 to 48 hours of receiving your Excel files. I look forward to reviewing your dataset structure!

@caajoys

I can build a clean, reproducible Python classification workflow that takes your Excel data from raw rows through preprocessing, model training, validation, evaluation, and a reusable saved model. Thank you for considering my proposal. I have gone through the requirements in detail. I’ll load and inspect the Excel data with pandas, handle missing values, duplicates, data types, and relevant preprocessing, then establish an appropriate baseline classification model using scikit-learn. I’ll evaluate it using suitable metrics such as accuracy, precision, recall, F1-score, and a confusion matrix, with visual checks where useful. Where the data supports it, I can add practical feature engineering, cross-validation, and hyperparameter tuning while avoiding unnecessary complexity or overfitting. The final notebook/script will be clearly documented so you can rerun the complete pipeline with fresh data. I’ll also provide a joblib/pickle model and generated evaluation outputs where appropriate. I have uploaded samples similar to this project completed by me earlier in my profile. With 10+ years of experience, I am a Chartered Accountant (ICAI) and CPA with strong expertise in analytical modelling, Excel, Python-based data processing, financial analysis, and business reporting. I’m ready to review the Excel dataset and build a reproducible classification pipeline around its structure and target variable.

@dushyant317

Hi, we can build a complete, reusable Python ML classification pipeline from your Excel data using Pandas, NumPy and Scikit-learn. We’ll handle data cleaning, preprocessing, feature engineering, model selection, train/validation, hyperparameter tuning, and evaluation with accuracy, precision, recall, F1, confusion matrix and relevant plots. You’ll receive a clean Jupyter Notebook or commented Python script, README, saved Joblib/Pickle model, and all required reports/plots for applying the model to new data.

@alluankit01

Hi, I can build a complete Python classification workflow from your Excel dataset, covering data cleaning, feature preparation, model training, validation, tuning, and reusable prediction code. My approach is to first inspect the sheets, target variable, missing values, categorical fields, class balance, and potential data leakage. I’ll then build a reproducible scikit-learn pipeline and compare suitable models such as Logistic Regression, Random Forest, Gradient Boosting, or other classifiers based on the dataset. I’m comfortable with Python, pandas, NumPy, scikit-learn, Jupyter Notebook, feature engineering, preprocessing pipelines, hyperparameter tuning, cross-validation, and classification evaluation. Deliverables will include: * Clean Jupyter Notebook or .py script * Excel data loading and preprocessing * Missing/categorical data handling * Feature engineering where useful * Train/validation workflow * Model comparison and tuning * Accuracy, precision, recall and F1 metrics * Confusion matrix and evaluation plots * Saved model using joblib/pickle * README/reuse instructions * Clearly commented, reproducible code I’ll focus on avoiding overfitting and data leakage while producing a model you can easily retrain and use with fresh Excel data. Best regards Ankit

@braing2

Hello, I built 7 products in 7 days, and every client I've worked with on Freelancer has said they'd hire me again. All the cleaning, encoding and scaling will sit inside one scikit-learn Pipeline, fitted only on the training rows, so no information from the test set leaks in and the scores you see are honest. I'll start with a simple baseline that always guesses the most common class, so you can see how much the model actually adds, then compare logistic regression with gradient boosting under stratified cross-validation. If your classes are uneven, I'll report precision and recall per class next to accuracy, with a confusion matrix. The whole fitted pipeline is saved with joblib, plus a small predict script, so a fresh Excel file goes through exactly the same steps. Amul

@ronakkataria1

The step that usually breaks a model like this comes after handover, when you feed it a fresh Excel file. If cleaning and encoding happen in pandas before training, say pd.get_dummies on a category column, the new file produces different columns. A category missing this month drops a column, a new one adds one, and the model either errors or quietly reads features in the wrong order. So every step lives in one scikit-learn Pipeline: imputing, scaling, and one-hot encoding that ignores unseen categories, saved with the model as a single joblib file. Scoring new data is one call on the raw sheet. The test rows are split off before any cleaning statistics are fitted, so nothing leaks from them. Model choice comes from cross-validated scores against a trivial baseline, then tuning on the winner, with accuracy, per-class precision and recall, a confusion matrix and plots. A README walks through each step and how to score new files. Similar work: classification models for credit decisioning, bank customer churn and card fraud, mostly XGBoost and random forests in Python. My rating here is 5 out of 5. Which mistake costs you more: flagging a row that's actually fine, or missing one that isn't? That sets the threshold and the metric to optimise. Ronak

@alfatrade2

I would ship a notebook that reloads a fresh workbook through the same cleaning and a saved joblib model, then prints precision-recall and a confusion matrix rather than accuracy alone. The brief never names the target column or whether multiple sheets share one schema, so an id or date field can leak and inflate the score. Which column is the label, and are the sheets one table?

@prachia77

Hi, I can build a complete, reusable Python classification pipeline from your Excel dataset, covering data loading, cleaning, preprocessing, feature engineering, model selection, training, validation, and evaluation. I’ll use pandas, NumPy, and scikit-learn with appropriate visualization tools for data exploration and performance analysis. I can compare suitable classification algorithms, apply hyperparameter tuning where beneficial, and provide metrics such as accuracy, precision, recall, F1-score, and confusion matrix so the model’s performance is easy to understand. You’ll receive a clean Jupyter Notebook or well-structured Python script, a clear README/inline documentation, and a serialized model using joblib/pickle where appropriate, ready to reuse with new data. Best Regards, Webix Infotech India Pvt. Ltd.

@salihkarademir17

Hi, this is a clean, well-defined job and I can deliver it as a commented notebook plus a reusable .py script. How I would build it: 1. Load and audit: read all sheets with pandas, check types, missing values, duplicates, class balance and any column that leaks the answer (a common cause of "too good to be true" accuracy). 2. Prepare: a scikit-learn Pipeline (imputation, scaling, one-hot encoding for categories) so the exact same steps run on fresh data later. 3. Model: a stratified train/test split, then compare a simple baseline (logistic regression) with tree ensembles (random forest, gradient boosting) using cross-validation, and tune the best one with a small grid or random search. If the classes are imbalanced I use class weights and judge by F1 / precision-recall, not accuracy alone. 4. Report: accuracy, precision, recall, F1, a confusion matrix, a precision-recall curve and feature importances, each with a one-line plain-English reading. 5. Reuse: the whole pipeline saved with joblib and a predict script: point it at a new Excel file and it writes the predictions to a new sheet. The README explains every step. Fixed INR 15,000, delivered within 3 days of receiving the data. Roughly how many rows are there, and which column is the target?

@rakhshanz3

Having worked extensively with Machine Learning (ML) and Software Architecture throughout my career, I have the expertise you need to create a powerful classification model from your Excel dataset. My strong background in academia, teaching and research equips me with thorough problem-solving skills to effectively clean, prepare, and model your data. In addition, my professional experience includes end-to-end development of AI-driven solutions at scale. I’ve delivered Machine Learning based analytics platforms for business insights, customizable AI tools for data extraction and recommendation systems and API integrations - all key elements your project requires.

@abhilashr53

I can deliver 'Python Classification Model from Excel' end to end — clean architecture, tests, and deployment. I'll start with a short plan, share progress daily, and hand over production-ready code. Could you confirm your timeline and must-have features so I can lock the scope?