Saurabh askedon {{::getFormatedLocalTime("2018-10-22T12:29:11.677Z", {without_time: true})}}
Any openings in data science and machine learning? LinkedIn: https://www.linkedin.com/in/saurabh-kamble-52791314a/
I have 12 months of Experience with Python[Numpy,Pandas,MatplotlibSeaborn,Sklearn] and R programming. I have worked on Machine Learning , Natural Language Processing, Deep learning, Data Visualization tools like Tableau , QlikSense , Advance Excel and MySQL, looking for applying these skills on real-time data.Technical Skills:Applied Statistical Modelling for models analysing Z-score, Skew and Kurtosis, Data Transformation, Hypothesis Testing, Using ML Algorithms like Linear/Logistic Regression, Decision Trees, Random Forest,Xgboost,Naive-Bayes,PCA,SVM,K-means,KNN including working on Natural Language Processing with TensorFlow. Sentiment Analysis on IMDB movie dataset, Image Classification using CNN, Customer Segmentation/Churn Analysis.
Building a ML model without the rite data is like buying shoes without knowing which size. (Sorry, poor anology :P) . Having said that, there are multiple things you need to consider.
1. If it is early stage and you have very little data, more often than not you may not even need a ML model. For example, if you are a E-comm company with few products and few users, you can not build a reccommendation engine and you do not have to also. You can make do with good old if-else conditions or hand crafted rules.
2. If your product does require you to show some Proof of concept that you can pull off a particular model; than try to get some data that looks similar to your problem and develop a model on it. Say if you are into Data science consulting and you want to impress a new Digital marketing client with your swanky new click through rate than show that your model can do well on this criteo dataset.
3. As far as I know, there is no staright forward way to starting with a simple model on small data and gradually increasing the complexity as the data gets complex. It is much like building multiple models with multiple datasets.
4. You may want to try to genreate a fake dataset if you know what kind of characteristics your features you would be having. For ex: predicting the grade of a student; given features like attendace, marks in various subjects etc. (Simple example, but you get the idea!)
I think we can help out much better if you can tell what kind of problem is that you are solving. :)