renee127 / learning-linux-command-line-3005201

Learning Linux Command Line

👋 Hi, I'm Renee

• 🌱 I’m starting the PhD program (computer science) at Florida Atlantic University in Spring 2023
• 👩‍🎓 MS in Data Science and Analytics at Florida Atlantic University December 2022
• 👩‍💻 Data Scientist Certificate from Practicum by Yandex (Thank you Women Who Code for the Scholarship!) April 2022
• 🔎 I'm looking for a full time remote job
• 📫 How to reach me: [email protected] or https://www.linkedin.com/in/renee-raven/

Collection of programming assignments completed for Practicum's Data Scientist professional training program:

Project Name	Notebook	Description	Dependencies	Sprint Number
classifying_churn	classifying_churn.ipynb	Used machine learning and data balancing techniques to create a predictive model for churn producing an AUC-ROC higher than the target AUC-ROC (0.93 versus goal of > 0.88).	NumPy, Pandas, matplotlib, seaborn, math, time, functools, re, IPython.display, sklearn, catboost, lightgbm, xgboost, random, sys	15 (final)
computer_vision	computer_vision.ipynb	Use supplied photos and starter code to build and test a regression model that predicts age (on a continuous scale) from a photo.	Pandas, Seaborn, matplotlib, tensorflow, keras	14
ml_for_text	ml_for_text.ipynb	Train a model for classifying positive and negative reviews with a F1 score of at least 0.85.	NumPy, Pandas, matplotlib, seaborn, re, math, tgdm	13
time_series	time_series.ipynb	Use historical data on taxi orders to predict peak hours using RMSE as the metric.	NumPy, Pandas, matplotlib, sciPy, seaborn, time, math, statsmodels, sklearn, IPython, sys, catboost, lightgbm, xgboost	12
numerical_methods	numerical_methods.ipynb	Generate a model that predicts the value of a car based on historical data (such as trims, prices, milage, technical specs)	NumPy, Pandas, matplotlib, seaborn, time, math, sklearn, random, sys, catboostregressor, decisiontree	11
linear_algebra	linear_algebra.ipynb	Use ML to categorize customers, identify customers likely to receive an insurance benefit, and use data masking.	NumPy, Pandas, math, seaborn, matplotlib, sklearn, IPython, sys	10
ml_in_industry	ml_in_industry.ipynb	Find the ML model that best predicts the two target values given the predictor variables present for gold extraction from ore.	NumPy, Pandas, math, seaborn, matplotlib, sklearn, random, sys	9
ml_in_business	ml_in_business.ipynb	Use machine learning and boostrapping to select a region with the highest profit margin given a selection of masked features.	NumPy, Pandas, math, seaborn, matplotlib, sklearn, scipy, random, sys	8
supervised_ml	supervised_ml.ipynb	Predict customer churn for a bank.	NumPy, Pandas, math, matplotlib, sklearn, random, sys	7
machine_learning	machine_learning.ipynb	Creat a ml model that recommends an appropriate plan based on data about the behavior of subscribers who've already switched (with accuracy > 75%)	NumPy, Pandas, sklearn, sys	6
sql	sql.ipynb	Identify top neighborhoods in terms of drop-offs for a new ride sharming company.	NumPy, Pandas, matplotlib, seaborn, scipy	5
hypothesis_testing	hypothesis_testing.ipynb	Preliminary analysis of platform, genre, and ESRB ratings to determine any patterns that influence sales.	NumPy, Pandas, matplotlib, sciPy, seaborn	4
statistical_data_analysis	statistical_data_analysis.ipynb	Analysis of phone plans, revenue, and retetion to produce recommendations for the marketing team.	NumPy, Pandas, matplotlib, sciPy	3
exploratory_data_analysis	exploratory_data_analysis.ipynb	Use EDA to study data collected over the last few years from online advertisements and determine which factors influence the price of a vehicle.	NumPy, Pandas, matplotlib	2
credit_scoring	15_credit_scoring_sprint_1.ipynb	Create a credit score for potential customers for a loan examining marital status and number of children as features.	NumPy, Pandas	1

Authors

Renee Raven

License

This project is licensed under the MIT License - see the LICENSE file for details