Week 8 ( 20/08/2018 - 24/08/2018)

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Day 1 - Numpy Revision

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Numpy Revision  We made a python program for the following random walk question: Say you are standing at the bottom of a staircase with a dice. With each throw of the dice, you either move down one step (if you get a 1 or 2 on the dice) or move up one step (if you get a 3, 4 or 5 on the dice). If you throw a 6 on the dice, you throw the dice again and move up the staircase by the number you get on that second throw. Note if you are on the base of the staircase you cannot move down! What is the probability that you will reach more than 60 steps after 250 throws of the dice? This question required the knowledge of np.random.seed. It s eeds the generator.  It makes the random numbers predictable. When the value is reset, the same numbers will appear every time. If a  seed is  not assigned , NumPy automatically selects a random seed value based on the system's random number generator device or on the clock The code was as follows: import numpy as np n...
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Day 3 - Introduction to Logistic regression

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Introduction to Logistic regression  While we continued to write the numpy code for linear regression we were introduced to Logistic Regression. It  i s a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes). It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent the binary/categorical outcome, we use dummy variables. You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as the dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a  logit  function. It required us to understand sigmoid function. I n order to map predicted values to probabilities, we use the   sigm...
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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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