Practicing Data Science in Field

Let's data be the truth.

Halite 3 Beginner

Halite 3 Beginner

Halite 3 Beginner

Halite is an open source artificial intelligence challenge, created by Two Sigma.

Halite III is a resource management game. Where the goal is to build a bot that efficiently navigates the seas collecting halite, a luminous energy resource.

Here is my competition profile as mineshj1291.

I am trying to propose here few strategies and implementation details. You are always welcome to comment below.

read more

Google Colab Beginner

Google Colab Beginner

Google Colab Beginner

We can open Colab here : https://colab.research.google.com/

After opening the first thing we want to do will be importing data:

Data from Google Drive to Colab:

  • Mounting Google Drive : 
    from google.colab import drive
drive.mount('/gdrive')

Data / Code from Git repositories to Colab:

!git clone [git-repo-url]

Data from Kaggle to Colab:

!pip install kaggle
!kaggle competitions download [competition-name]

for more : https://github.com/Kaggle/kaggle-api

Data from Internet / Web to Colab:

!wget [web-url]

Data from local computer:

from google.colab import files
uploaded = files.upload()

read more

My Kernel Series

My Kernel Series

My Kernel Series

For Kaggle House Pricing Competition solving with Advanced Linear Regression Under Construction…

  1. Minimal Kernel LB: 0.60109
    • NaN => Median
    • LinearRegression
  2. Minimal + Normalized X Kernel LB: 0.30013
    • LinearRegression(Normalized X)
  3. Minimal + Normalized X,y Kernel LB: 0.14305
    • y = log2(y)
  4. Minimal + Normalized X skew,y Kernel LB: 0.14104
    • X = log2(X) if abs(skew) > 1.7 & no Inf issues
  5. Minimal + Normalized X skew,y + filter low Var Kernel LB: 0.13764
    • filter X if Variance < 0.2 and not correlated with target y

ElasticNet enters…

  1. Normalized X,y + dummy Kernel LB: 0.13817
    • dummy categorical features
    • ElasticNetCV
  2. Beginner ElasticNet Kernel Ver.1 LB: 0.13343
    • all previous things plus
    • ElasticNetCV alpha optimization
  3. Beginner ElasticNet + Univar_models Kernel Ver.2 LB: 0.13101
    • ElasticNetCV L1_ratio = 1
    • ElasticNetCV alpha optimization
    • Bagged with 1/3 Simple Linear Regression using selected features by Univariate model performance TestMSE < 0.5
  4. Beginner ElasticNet + Univar_models Kernel Ver.3 LB: 0.12867
    • Previous kernel with thresold 0.55 instead on 0.5
    • ver4 LB: 0.13976 didn’ improve
    • Bagged with 2/3 * ver5 LB: 0.13131
    • Bagged with 1/3
    • Thresold 0.55
    • ElasticNetCV L1_ratio = 0.2

  5. Beginner ElasticNet + Univar_models Kernel ver.7 LB: 0.12860

  6. Beginner ElasticNet Kernel Ver.5 LB 0.12811
    • ElasticNetCV L1_ratio = 0.1
    • ElasticNetCV alpha optimization
    • Bagged with 5. Minimal + Normalized X skew,y + filter low Var Kernel LB: 0.13764
  7. Beginner ElasticNet Kernel Ver.7 LB 0.12408
    • ElasticNetCV L1_ratio = 1
    • ElasticNetCV alpha optimization
    • Bagged with
    1. Minimal + Normalized X skew,y + filter low Var Kernel LB: 0.13764 and
    2. Beginner ElasticNet Kernel Ver.5 LB 0.12811

Next things in the list are:

  • max_iter=1000, tol=0.0001 ? optimize
  • positive & Selection with t<1e-8
  • NaN Imputation
  • Outlier Remove
  • Ensemble

Under Construction…

read more

Let's start with a well-known dataset

Let's start with a well-known dataset

  • We will start to build simple linear regression model for Predicting house pricing.
  • Description about the House Pricing Data, you can find on Kaggle Competition page
  • You can try running simple linear regression model using only 2 features on kaggle kernel
    • This is a very simple model to begin, in future we will perform data cleaning, exploratory analysis and advanced modelling techniques, etc.
    • But at this level you can try fork and run the given kernel, main goal here is to introduce you to simple python script for modelling using pandas, numpy and scikit learn.
    • To improve model first you can note down current RMSE for model, then you can try using other feature than which we already used and have hands-on experiance.
    • If you encounter any problem you can ask in comment with script sniplet and output.

read more