Le Wagon Tokyo’s Post

Dear Network, Six months ago, I embarked on a journey with Solstice Lab, a Paris-based data consulting firm, as a Data Scientist. 🚀 My time at Solstice surpassed my expectations, providing me with invaluable experiences in consulting and data science. 📊 🤔 Reflecting on my internship as a Data Scientist at Solstice, I realized and experienced a few things that I think are worth sharing: 1️⃣ Striking the Balance with Machine Learning/AI: Practicality Over Fancy Solutions 🤖 I've learned the importance of being pragmatic and embracing simplicity. Not every problem demands a complex solution; sometimes, a straightforward approach is the most effective. Avoiding "overengineering" is key. 2️⃣ Building Bridges between Tech and Business Realities 🌉 One of the significant challenges I encountered was the disconnect between technology and business components. Often, they don't speak the same language. The real value lies in making both sides understand each other, and I've witnessed firsthand the impact of fluency in both tech and business at Solstice Lab. 3️⃣ Navigating the Tech Landscape: The Imperative of Agility and Flexibility 🧭 In the ever-evolving tech landscape, staying updated is paramount. New technologies emerge, existing ones improve, and it's essential to remain agile and learning to stay up-to-date. I personally find the Low Code/No Code domain particularly intriguing and worth exploring. Thanks to Solstice Lab for this wonderful opportunity. 🙏 After Data Science, my next station is Venture Capital. I'm joining Saviu Ventures as an Analyst for the second part of my gap year. Over the past year, I have been constantly immersed in the entrepreneurship ecosystem. I'm very thrilled to have the opportunity to break into VC. A big thank you to the entire Saviu Team for the warm welcome and their trust. 🌟 #VentureCapital #DataScience #DataDrivenVC #VentureCapitalAfrica

Hello, Connections!! The second task of my internship wrapped up! 🚀 It's been an incredible journey of learning and growth. On to the next challenge 📌 Alfido Tech! " #DataScience #InternshipMilestones #LearningAndGrowing  Alfido Tech #Task 2: IRIS FLOWER CLASSIFICATION Steps Performed : Data Exploration (EDA): Conduct exploratory data analysis to understand the dataset, including summary statistics, distributions, and visualizations. Data Preprocessing: Prepared the data for modeling by handling missing values, encoding categorical variables, and feature engineering. Feature Selection: Identifying which features are most relevant for predicting survival. Encoding: Converting categorical data (non-numeric data) i.e. Species Column into a numerical format that can be used for analysis and model training. Correlation: Includes feature selection, dimensionality reduction, identifying multicollinearity in regression models, and understanding the relationships between variables in exploratory data analysis (EDA). Train_Test_Split: The train-test split is a crucial step in model development, as it allows you to estimate how well your model is likely to perform on unseen data. Model Selection: Compared appropriate machine learning algorithms for classification tasks, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, KNN,Gradient Boosting Classifier Model Training: Trained multiple models on a training dataset, optimizing hyperparameters as needed. Model Evaluation: Evaluated the models using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score) and visualized the results.

Hello, Connections!! The second task of my internship wrapped up! 🚀 It's been an incredible journey of learning and growth. On to the next challenge 📌 Alfido Tech! " #DataScience #InternshipMilestones #LearningAndGrowing  Alfido Tech #Task 2: IRIS FLOWER CLASSIFICATION Steps Performed : Data Exploration (EDA): Conduct exploratory data analysis to understand the dataset, including summary statistics, distributions, and visualizations. Data Preprocessing: Prepared the data for modeling by handling missing values, encoding categorical variables, and feature engineering. Feature Selection: Identifying which features are most relevant for predicting survival. Encoding: Converting categorical data (non-numeric data) i.e. Species Column into a numerical format that can be used for analysis and model training. Correlation: Includes feature selection, dimensionality reduction, identifying multicollinearity in regression models, and understanding the relationships between variables in exploratory data analysis (EDA). Train_Test_Split: The train-test split is a crucial step in model development, as it allows you to estimate how well your model is likely to perform on unseen data. Model Selection: Compared appropriate machine learning algorithms for classification tasks, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, KNN,Gradient Boosting Classifier Model Training: Trained multiple models on a training dataset, optimizing hyperparameters as needed. Model Evaluation: Evaluated the models using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score) and visualized the results.

🚢 Exciting Data Exploration Journey Ahead! 🌊 Ahoy, LinkedIn community! 🌟 Today, I'm thrilled to embark on an incredible data adventure that's as legendary as the Titanic itself! 🚢⚓ I've set sail on a journey through the vast ocean of Titanic data, delving into its rich history and uncovering fascinating insights. 📊 From passenger demographics to survival rates, this dataset holds a treasure trove of information waiting to be discovered. 🤓 As we navigate these data waters, I'm excited to share my findings and insights. This is a fantastic opportunity to learn, grow, and connect with fellow data enthusiasts. 🌐 But no voyage is complete without the right crew, and that's where BharthIntern comes in! 🌐🤝 I'm honored to partner with BharthIntern, a platform dedicated to nurturing talent and fostering connections in the data world. With their support, we're steering this ship in the right direction. 🌐⚙️ Stay tuned as we unravel the mysteries of the Titanic dataset and explore the power of data analytics together. 🚀 Feel free to join in and share your thoughts and insights using #TitanicDataJourney and #BhartnIntern. Let's make this journey one for the books (or should I say, databases)! Bon voyage! 🌊🌐⛵ #DataExploration #DataAnalytics #TitanicData #DataScience #BharthIntern

🔍 Decoding Data Realms: A Voyage with TechnoHack 🚀 An electrifying update from my corner of the universe: I've embarked on an extraordinary internship expedition with TechnoHack, navigating the uncharted territories of Data Analytics. Prepare to witness data unveil its enigmatic stories! 📊 Data Alchemy in Progress: Imagine the thrill of transforming raw data into goldmine insights! In this role, I'll be orchestrating data symphonies, conducting intricate analyses, and distilling complexity into clarity. Collaborating with the ingenious minds at TechnoHack, I'm set to master the art of translating numbers into actionable wisdom. 🌌 Architecting the Future with Data Blueprints: Data isn't just information; it's the blueprint of the future. I'm absolutely exhilarated to wield advanced tools and methodologies, shaping data into a force that drives strategic decision-making. Guided by mentors who are virtuosos in this realm, I'm on a trajectory to emerge as a skilled data architect. 🤝 A Gratitude Galaxy: I extend my heartfelt gratitude to the TechnoHack constellation for bestowing upon me this stellar opportunity. Their commitment to nurturing growth and innovation resonates deeply, and I'm poised to absorb their wealth of knowledge and expertise. 🌟 Join the Cosmic Quest: Hold tight as I embark on this cosmic journey! Brace yourself for insights, breakthroughs, and perhaps even a celestial surprise or two. Your support and engagement propel me to excel in the dynamic and captivating universe of data analytics. Ready to traverse the galaxies of data together? 🌠📊 TechnoHacks EduTech Official Sandip Gavit #DataAnalyticsExpedition #InternshipAdventures #TechnoHackElevation #ChartingNewCosmos

Exciting News: I'm delighted to announce that I recently participated in the Randstad Technologies Italia AI Hackathon, hosted by VGen at Università degli studi Roma TRE. This energizing event featured a real-world challenge from Randstad Italia, involving the categorization of job offers using specific labels. With over 2,000 job postings and five distinct reference labels in the dataset, our goal was straightforward: to build an intelligent system for accurately classifying new job descriptions. My approach included creating a Python-based job classification model using libraries like Pandas and scikit-learn, along with advanced vectorization techniques such as Random Forest Tree, CountVectorizer, and TfidfVectorizer. Our efforts paid off despite challenges during the training phase and while evaluating the model's performance using precision, recall, and F1-score metrics. I greatly appreciate the organizers' meticulous planning and the provision of a well-structured dataset. Working with curated data was transformative, providing the ideal platform for participants to showcase their abilities. This hackathon was more than just a competition; it encouraged growth and learning. Congratulations to the organizers for providing this platform for us to enhance our abilities. Let's look forward to more enriching experiences in the future! 🌟  #DataScience #TechInnovation #LearningAndGrowth #AIHackathon #python #ai #AI #MachineLearning #DataScience #Randstad #Hackathon #JobClassification #Python #Pandas #ScikitLearn #RandomForestTree #CountVectorizer #TfidfVectorizer #RomaTreUniversity #Collaboration #Teamwork #SkillsDevelopment

🎉 Delighted to Share: Just Completed My Third Task as a Data Science Intern at Oasis Infobyte! I'm thrilled to announce the successful completion of my third task as a Data Science Intern at Oasis Infobyte! 🚗💼 Task Name: "Developing a Machine Learning Model for Car Price Prediction" Objective: Crafting a sophisticated machine learning model to predict car prices based on key features including Car Name, Year, Fuel Type, Transmission Type, Selling Price, Present Price, Ownership Type, Selling Type, and Driven Kms. This task has been a remarkable journey, and I've gained invaluable insights into data analysis, model development, and predictive analytics. I'm sincerely appreciative of the Oasis Infobyte team for their unwavering support and mentorship. 📦 For those interested in the technical details, the project code is available on my GitHub: [https://lnkd.in/dUu9TcKZ] 📓 You can also dive into a comprehensive analysis of the project on nbviewer.org: [https://lnkd.in/drhCBn2r] I'm looking forward to taking on more exciting challenges, expanding my horizons, and contributing to the innovative work underway at Oasis Infobyte. Thank you for being a part of this incredible journey! 🌐🔥 #DataScienceIntern #MachineLearning #CarPricePrediction #DataScience #oasisinfobyte #DataScienceJourney #GitHub #nbviewer #DataAnalysis #RegressionModels

Diving into the realm of data science at Oasis Infobyte! 🚀 My internship project involved crafting a machine learning model to classify iris flowers—setosa, versicolor, and virginica—based on their distinct measurements. 🌸 Leveraging the Scikit-learn library and real-world data, this endeavor honed my skills in iris flower classification. Excited to share the results and insights gained from this hands-on experience! 📊 #DataScience #MachineLearning #InternshipJourney #OasisInfobyte #IrisFlowerClassification Github link for this repository : https://lnkd.in/g7YJzm7y Download the dataset from: https://lnkd.in/gzRacMzq

Undoubtedly one of our proudest milestones in 2 years of Aya...congratulations Oswald Adzakpah, Teidor Alafegha-Quakumey and all other recently promoted DS interns. So much incredible stuff is happening in Africa to create a brighter Future of Work using technology (a brilliant example here highlighted by Matthew Grollnek on the impending return of personalised learning via AI - https://lnkd.in/eSkTy2zY). Our small contribution is in trying to create a model where people can move from low / medium skill jobs to high skill jobs - whilst creating extrinsic value. The latter half of this equation absolutely key as it unlocks genuinely impactful for-profit models that can scale quickly. At Aya you can now join our data annotation teams and promote directly into our data science and engineering teams, to work on some of the hardest problems in Geospatial and Generative AI. We still have a mountain to climb, but it's great to be officially taking this step along with Ama LS and the rest of our brilliant team. If you want to learn how to identify mangrove trees from leaf shapes using machine learning and a drone, get in touch! 😁 🌳 🤖 #futureofwork #aiinafrica #geospatialtech #climatetech