Salam, data science dost! Aaj hum guftagu karein gay do bohat ahem topics par jo data science aur machine learning (ML) ke duniya mein har kadam par sath detay hain: EDA (Exploratory Data Analysis) aur data pre-processing. 1. EDA (Exploratory Data Analysis) Ki Importance 🕵️♂️ Jab aap kisi naye dataset se milte hain, toh sabse pehla kaam kya hota hai? Bina sochay samjhay ML model train karna? Nahi, yaar! Pehla kaam jo humein karni chahiye, woh hai EDA. - Data Ko Samajhna: 🧠 EDA ke zariye, hum apne data ko behtar samajh sakte hain: konsay variables hain, inki range kya hai, aur inme kaise patterns moujood hain. - Mushkilat Ko Pehchanna: 🧐 EDA ke through, hum anomalies aur unusual points ko pehchaan sakte hain, jo model training mein problem create kar sakte hain. - Data Ki Kahani Sunna: 📖 Har dataset mein ek kahani hoti hai. EDA ke zariye, hum woh kahani ko samne laa sakte hain. - Exploratory Data Analysis k liay ye blogs zaroor parhen https://codanics.com/a-desi-guide-to-exploratory-data-analysis/ 2. Data Pre-processing: ML Ke Liye Data Ki Tayyari 🛠️ Agar EDA data ko samajhne ka pehla qadam hai, to data pre-processing woh qadam hai jisse hum data ko machine learning ke liye ready karte hain. - Data Ki Safai: 🧹 Socho, agar data mein masail hain, to kya hum acha model train kar sakte hain? Isliye, missing values aur unusual points ko handle karna padta hai. - Data Ko Convert Karna: 🔄 Machines sirf numbers samajhti hain, isliye humein text data ko numeric form mein convert karna parta hai. - Feature Engineering: 🔧 ML models ke liye, hum naye features bhi tayyar karte hain taake model ki performance improve ho. 3. EDA aur Data Pre-processing: Data Science aur ML Ka Backbone 💪 Behtareen Model Training: Sahi data preprocessing se ML models ki accuracy aur performance behtar hoti hai. Waqt Bachao: ⏳ EDA aur data pre-processing ke zariye, hum future mein hone wale masail ko pehle hi pehchaan kar door kar sakte hain. Conclusion: Data Science Aur ML Ki Safar Ka Saathi 🚗 Agar EDA aur data pre-processing na hoti to? ML projects mein kai challenges aate. Lekin in dono tools ke saath, aap data science aur ML mein behtar tajurba hasil kar sakte hain. Aakhir mein yaad rahe: Aik Machine Learning model sirf utna hi acha hota hai jitna uska data. EDA aur pre-processing ke zariye, hum is data ko behtar bana sakte hain. Happy coding! 🚀🎉 https://codanics.com/pandas-python-library-for-eda-a-comprehensive-guide/
