zigjagcoder.py
@zigjagcode
indention matters || AI , ML || frontend developers aren’t programmers prove me wrong
Day 13 of becoming AI/ML god Today i revised my projects and push it on github Final out from this HOUSING PRICE PRIDICTION MODAL 1. Best model for this is forest regression 2. RMSE is ~$47,730 Link for the finals project is github.com/bunny5058/ML-p…
Looking to connect for some tech guys and girls 👧 Currently - 66 followers Aim - 100 followers in 24 hours from now Let’s grow together #letsconnect #100DayChallenge
Every programmer is a gamer as well ??? Mostly competitive games like velo ?
🔖400 posts 🧑⚖️50 followers Doesn’t sounds great but it is as it is 😁🤩 #letsconnect #100DayChallenge
🤩Day 12 of becoming AI/ML god Today i Learnt about : 🔵 manually using test and validation❌ 🔵built in function in sklearn ✅ 🔵 it’s cross_val_score 🔵 number of train and validation set by an argument “cv” #100DaysOfCode #100DayChallenge
🤩Day 11 of becoming AI/Ml god 🧠I wrote a function which fills the missing values in a data from median of its columns Using sklearn in which SimpleImputer Share me any other typ of inplace values for missing values that a ml engg. Would prefer
🤩Day 10 of becoming AI/Ml god 🧠Started house_pricepridiction project In which i learnt 1. load the data for california 2.trying to notice patterns 3.STANDARD CORRELATION COFFICIENT 5. Straitified_shuffle by sklearn #100DayChallenge #100DaysOfCode
Hello 2:00am gang 🤩Day 9 of becoming AI/Ml god 💻Today I learned few things 1. Frame the problem clearly ✅ 2. Examine current solution ✅ 3. Selecte a performance measure✅ i) RMSE ii) ASE #100DaysOfCode #100DayChallenge
Hello 2:30AM gang .. 💻Day 8 of becoming AI/Ml god After #INDvPAK it’s time for grind at ⏰2:30am Learnt about: 1.preparing data ✅ 2.oversample (RandomOverSample)✅ 3. KNN and classification_report ✅ #100DayChallenge #100DaysOfCode #AIMl
💻Day 7 of becoming AI/Ml god 🧠 Supervised Learning – guiding the model with labeled data ✅ Classification – predicting categories (spam or not spam) 📈 Regression – predicting continuous values (house prices) 🚀 Tomorrow, we go deeper! #100DayChallenge #100DaysOfCode
🤩Day 6 of becoming AI/Ml god 💻Learnt about problems while choosing best model and data 🧠Learnt about 📖 1.overfitting, underfitting 2. Training set , testing set 3. Holdout validation 4. Generalisation error 💪Readyy for the next day
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