Aviation & Missile Solutions’ Post

🚀 Excited to share that I've completed my final task (TSK-000-188) at Internee.pk ! Model Evaluation Description: Performance Metrics: 1. Select relevant evaluation metrics based on the specific problem, such as accuracy, precision, recall, F1-score, or mean squared error. 2. Consider using ROC curves and AUC for classification tasks or R-squared for regression tasks. Model Interpretation: 1. Interpret model results and coefficients to understand the impact of features on predictions. 2. Utilize feature importance plots, SHAP values, or LIME for black-box model interpretability. Model Validation: 1. Validate model performance on a separate test dataset to assess real-world predictive capabilities. 2. Evaluate model robustness and stability through techniques like bootstrapping or Monte Carlo simulations. Grateful for the opportunity to enhance my skills and work on such interesting projects!

This is my second task at #internpe , this code is for CAR PRICE PREDICTOR WITH ML.

During my internship at NSTL-DRDO, I had the opportunity to do a project at the intersection of Computational Fluid Dynamics (CFD) and Machine Learning. The focus was on integrating these technologies to create a predictive model for estimating drag on underwater bodies. This hands-on experience not only deepened my understanding of advanced fluid dynamics simulations but also allowed me to apply state-of-the-art machine-learning techniques to real-world challenges. I successfully completed the internship, gaining invaluable insights into the practical application of CFD and Machine Learning in the context of underwater systems. #NSTL #drdo

Say Hello to Keremalp Durdabak! Keremalp was our intern with OceanSync, a company that aims to install gateways to the vessels over the Atlantic that would eventually help ships interconnect with each other. Keremalp analyzed the gateway data from vessels to infer a relation between the features using plethora of machine learning methods to eventually predict weather elements. Through his work with OceanSync, he sharpened his machine learning skills and software engineering skills. Learn more about Keremalp here https://lnkd.in/erZNzmeJ

This is the second task for Mentorness internship. The task is to build a predictive model that can accurately classify mobile phones into predefined price ranges based on various attributes such as battery power, camera features, memory, connectivity options, and more. I have built 5 distinct models: Logistic regression, support vector classifier, decision tree classifier, random forest classifier, and gradient boosting classifier. I did hyperparameter tuning and cross validation with grid search. Then I have deployed the best performing model with Streamlit. The best performing model shows the 98% accuracy. This is the link: https://lnkd.in/gBGaaMCJ #logisticregression #supportvectormachine #decisiontree #randomforest #gradientboosting

InternPe Task 2 ✅ Task given: Car price prediction Technology Used: Linear Regression #internpe #internpeinternship #linearregression #prediction #regression

DEVELOPMENT OF CONTROL ALGORITHM FOR 3 AXIS HYDRUALIC ARM: This Internship project at end of 1st semester involved understanding the functional requirement of positioning of the hydraulic arm and developing a closed loop control algorithm for the same. The algorithm was to be eventually programmed and integrated to GUI. My scope of work was to develop the math and execution sequence that could be adapted into the program. Key challenges were in (i) using stepper motors to turn a physical hydraulic valve and integrate feedback from the arms into closed loop and (ii) Manage difference in upward motion and downward motion speed using correction factors. (iii) Take spatial co-ordinates and to break down and define target positions for individual axes.

Hello everyone, I am happy to share with you that I have completed #task3 at The Sparks Foundation as Data Science and Business Analytics Intern under the Graduate Rotational Internship Program (GRIP) GRIP@ The Sparks Foundation GRIP JANUARY 2024 #task3: Exploratory data analysis – retail Data: sample superstore Objective: The main objective of this project is to explore the data, extract the key insights and find out the weak areas where you can work to make more profit. Tool used: Power BI Steps involved: 1)   Import the data 2)   Clean the data 3)   Analyze the data 4)   Create a Dashboard 5) Draw the conclusion 🔗 YouTube link: https://lnkd.in/dzWDeVaa 🔗 LinkedIn article for the detail explanation: https://lnkd.in/dxpNa8xQ 🔗 Link for the ppt: https://lnkd.in/deWrn3TJ I am very thankful to The Sparks Foundation for giving me an internship opportunity. 🔗Apply Here for an Internship under The Sparks Foundation: https://lnkd.in/diDEqUQb Kindly go through my work and feel free to give your reviews. #TheSparksFoundation #intern #GRIP #sparksfoundation #DataScience #BusinessAnalytics #powerbi #dashboard #Internship #datascience #businessanalytics #EDA #exploratirydataanalysis #dataanalysis #gripjan2024 #gripjan24 #GRIPJAN24 #GRIPJANUARY24

"Recall the nostalgia of Duck Hunt? 🦆🎮 Well, brace yourself for a next-level experience! During my internship, inspired by my colleague Mithun Xavior , I reimagined this classic game in the real world. Say farewell to screens and welcome real-life adventure! Picture this: ducks maneuvered by servo motors, a laser gun for precise shooting, and all the excitement of the original game, now tangible. 🎯 Leveraging Arduino Uno and ESP8266, I brought this vision to life. It's a real game with ducks and a gun, where ducks are controlled by servo motors, LDR is used to detect the shot, and a laser gun is employed for shooting. Plus, with a Flask server integrated, real-time scores and remaining shots can be viewed on screen. It's like stepping into a virtual realm, but in the here and now! #fungame #TechInnovation #DuckHuntRevamped #arduinouno #ldr #laserled #servomotor #flask

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