Week 14 ( 01/10/2018 - 05/10/2018)

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Day 4 - Linear Regression Numpy code and Python course

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Linear Regression Numpy code   We finished coding generating data for the numpy version of Linear regression. We didn't use data from an excel sheet or a Kaggle dataset and hence we had to create our own data. For this, we created random integer data for our X and betas. Then we created a noise, as real data always has noise, using this we created Y data. the code for the same was as follows: import numpy as np samplesize=1000 num_attrs= 3 step = 0.1 x_inputs = np.random.rand(samplesize,num_attrs-1) x0 = np.ones((samplesize,1)) x_data = np.concatenate((x0, x_inputs), axis=1) noise = np.random.randn(len(x_inputs),1)  betas = np.random.rand(num_attrs,1) y_true = x_data.dot(betas) + noise  #understand this y_true.reshape(1000,1) Python course  We started an Udemy course on Python. The concepts we covered today were: Pros and Cons of Dynamic Typing String Indexing and Slicing Various String Methods String Interpolation:  ...
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Week 11 (10/8/18 - 14/8/18)

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After completing my news article recommender last week the upcoming week brought me opportunity to explore a library for high dimensional space visualization t-SNE , I was told by my mentor Mr.Vikram Jha to explore it and tell insights. t-SNE t-Distributed Stochastic Neighbor Embedding (t-SNE) is a ( prize-winning ) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. Before jumping to t-SNE i knew about old technique of dimensionality reduction that is PCA,  Principal Component Analysis, I first studied in ISB videos but when Sarabjot sir explained,it  became thorough PCA Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize.
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Week 21 (19/11/2018 - 23/11/2018)

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Ropar data hypothesis testing After drawing inferences from the data visualization, we needed to make sure that the results are true or not for a population(we were given a sample). So, we studied  hypothesis testing and then  applied it in order to find the dependency of the factors on each other and also with the number of accidents or fatalities. Hypothesis testing  is a statistical method that is used in making statistical decisions using experimental data.  Hypothesis Testing  is basically an assumption that we make about the population parameter. Various tests used for hypothesis testing are: z-statistic and p-value t-test Chi square test ANOVA Traffic Light Application Chandigarh's traffic police gave us the responsibility of designing an android application which regulated the time that should be given to a lane's cars to pass . It was based on many factors like time of red light, total time of amber light, etc. Firstly...
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