All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]

Anaconda is a distribution of Python, including a selection of libraries and other useful tools. It is not an IDE but does include the Jupiter and Spyder IDE

#sudo yum install python3

#sudo pip3 install pandas

#sudo pip3 install sklearn

•Preprocessing refers to transformation before feeding to machine learning

•Quality of data is important to train the model

•Source – Government databases, professional or company data sources(twitter), your company, etc

•Data will never be in the format you need – Pandas Dataframe for reformatting

•Columns to remove – No values, duplicate(correlated column, e.g: house size in ft and metres)

•Learning algorithms understands only number, converting text image to number is required

•Unscaled or unstandardized data have might have unacceptable prediction

•Check for Null values

•Remove or Impute

•df.isnull().values.any()

•df = df.dropna(how='any',axis=0)

•Sometimes two features that are meant to measure different characteristics of a model are influenced by common mechanism and they move together.

•

•How to Handle Correlation:

•Remove one of the feature

•Apply Principal Component Analysis(PLA)

•Adjusting Data Types - Inspect data types to see if there are any issues. Data should be numeric.

•If required create new columns

Missing Data - Ways to Handle

•Drop rows

•Replace values (Impute)

•Feature Scaling is a technique to standardize the independent features present in the data in a fixed range.

•It is performed during the data pre-processing to handle highly varying magnitudes or values or units.

•Disadvantage:

• Without Feature Scaling a machine learning algorithm tends to weigh greater values -> higher and consider smaller values as the lower values, regardless of the unit of the values.

Convert text values to numbers. These can be used in the following situations:

•There are only two values for a column in your data. The values will then become 0/1 - effectively a binary representation

•The values have relationship with each other where comparisons are meaningful (e.g. low<medium<high)

•Use when there is no meaningful comparison between values in the column

•Creates a new column for each unique value for the specified feature in the data set

Full Course content (Code) can be downloaded from Github: https://github.com/bansalrishi/MachineLearningWithPython_UD

•Error = (y_pred – y_act)^2

•Two Methods:

1.Least Square Criterian (OLS)

2.Gradient Descent

•non-iterative method that fits a model such that the sum-of-squares of differences of observed and predicted values is minimized

•Error = (y_pred – y_act)^2

•Line => y = bo + b1x

•Cost Function, J(m,c) = (y_pred – y_act)^2 / No. of data point

•Hypothesis: y_pred = c + mx

It tells how well regression equation explains the data.

•A value of R^2 = 1 means regression predictions perfectly fit/explains the data.

Question: Can ?2 be negative?

•Ans: When: ( Sum of Square Errors(SSE) > {Total Sum of Squares(SST)} )

•This means when our predicted model performs worst than average line which is a very rare case.

Code file and datasets can be found in the regression.zip.

1.Linearity

•linear regression is sensitive to outlier effects

•needs the relationship between the independent and dependent variables to be linear

•linearity assumption can best be tested with scatter plots

2. Homoscedasticity

•meaning the residuals are equal across the regression line

•Heteroscedasticity Test to check - The Goldfeld-Quandt Test

3. Multivariate Normality

•This assumption can best be checked with a histogram or a Q-Q-Plot

•Normality can be checked with a goodness of fit test(Kolmogorov-Smirnov)

4. No Autocorrelation in the Data

•when the residuals are not independent from each other.

•in simple terms when the value of y(x+1) is not independent from the value of y(x)

•Durbin-Watson test

5. Lack of Multicollinearity

•Multicollinearity: Model cannot differentiate between the effect of D1 and D2 as these are totally related.

•fixed using correlation in data pre processing

Assumption:
There is a linear relationship between both the dependent and independent variables.
It also assumes no major correlation between the independent variables.

•Multiple regressions can be linear and nonlinear.

y = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5

Here x4,x5 are dummy variable

x5 = 1 – x4

Multicollinearity -> that’s why its called as dummy variable

for 2 -> 1 & 0

For: > 2 -> column

If data is not linear, we need polynomial terms to fit it better

Issue with Linear Regression

•But if we have an outlier, it will go horribly wrong

•Because of one outlier, whole linear regression prediction is going wrong

Logistic Regression

• Logistic regression can be understood by standard logistic function. Logistic function is a Sigmoid function, which takes real value between zero and one.

• If we plot sigmoid function, the graph will be S curve. When there is an outlier, sigmoid function takes care of it.

• Linear regression assumes that the data follows a linear function.

• Logistic regression models the data using the sigmoid function

Describe the performance of a classification model

•Accuracy: Is fraction of correct predictions in all prediction made by model

•Precision: Is fraction of correct positive predictions in all positive predictions made by the model

•Recall: Is fraction of correct positive predictions made in actual positive data

Spam Filter (positive class: spam): Optimize for precision or specificity because false negatives (spam goes to inbox) are more acceptable than false positives (non-spam caught by spam filter).

Fraudulent transaction detector ( positive class: fraud): Optimize for sensitivity because false positives (normal transactions that are flagged as possible fraud) are more acceptable than false negatives (fraudulent transactions that are not detected)

It assumes that similar things exist in close proximity.

Algorithm:
* Step 1: Choose the no. K of neighbours
* Step 2: Take the K nearest neighbours of the new data points by Euclidean distance
* Step 3: Among K Neighbours, count the no. of data points in each category
* Step 4: Assign new data point to the category where you counted most neighbour

Its Naive(innocent) because it assumes that all the features are independent of each other. Which is almost never possible.

•Easy to understand.

•All features are independent.

•All impact results equally.

•Need small amount of data to train the model.

•Fast – up to 100X faster.

•It is highly scalable.

•It can make probabilistic predictions.

•It's simple & out-performs many sophisticated methods.

•Stable to data changes.

Algorithm:

1.Initialize k centroids.

2.Select at random K points, the centroids(not necessary from the dataset)

3.Assign each data to the nearest centroid, this step will create clusters.

4.Compute and place the new centroid of each cluster.

5.Reassign each data point to the new closest centroid. If any new reassignment, Repeat steps 4 otherwise go to Finish

•Solution(Fix) -> K-Means++

•K-Means++ -> smarter initialization of centroids, rest is same

WCSS:

•Euclidean distance between a given point and centroid to which it is assigned.

•Iterate this process for all the points in the cluster

•Sum all the values and divide by no. of points

Total WCSS decreases as no. of clusters increases

Total WCSS is minimum when No. of clusters is equal to no. of data points

Elbow Method to find the optimal number of clusters

•These methods does hierarchical decomposition of datasets.

•Agglomerative method (Bottom-Up): assume each data as cluster & merge to create a bigger cluster

•Divisive method (Top-Down): start with one cluster & continue splitting

Algorithm:

•Start with assigning one cluster to each data - N Cluster

•Combine two closest point in one cluster - (N - 1) Cluster

•Combine two closest cluster into one cluster - (N - 2) Cluster

•Repeat Step 3 until there is only one cluster left

All above techniques are distance based & such methods can find only spherical clusters and not suited for clusters of other shapes. All they are severely impacted by noise or outliers in the data.

Used:

•If data is of arbitrary shape

•Data contain noise

Algorithm has two parameters:
eps: The radius of our neighborhoods around a data point p. If distance between two points is lower or equal to eps then they are neighbours. Small value will lead to large data points as outlier and large value will lead to majority of data points to same cluster.

minPts: The minimum number of data points we want in a neighborhood to define a cluster. minPts >= D +1 and should be at least 3.

•Not as straight forward as Supervised Algorithm

•Question of Good clustering is relative

Some Popular Index:

Davies-Bouldin

•Evaluates intra-cluster similarity and inter-cluster differences

•Not Normalized, so difficult to compare between two different datasets

Silhouette Index

•calculates using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample

•The Silhouette Coefficient for a sample is (b - a) / max(a, b). To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of.

•Normalized, a value close to 1 is always good

•good for spherical data structures

Apriori Algorithm:

•Used to identify frequent item sets.

•Uses bottom-up approach, identify individual items first that fulfill a min occurrence threshold. After this, it add one item at a time and check if the resulting item set still meet the specified threshold.

•Algorithm stops when there are no more item left to add to meet the min. occurence threshold

•Once we generated itemsets using Apriori, we can apply association rules.

•As our item size is having 2 items so our association rule will be of the form (A) -> (B)

Three Stage:

1. Support

2. Confidence

3. Lift

•Its a tree like data structure to make a model of the data

•uses if-else at every node of the tree

•can be used for both classification and regression analysis

Algorithm : Decision Trees

•ID3 (Entropy and Information Gain)

•Gini Index

•Chi Square

My Github:  https://github.com/bansalrishi/MachineLearningWithPython_UD

For detailed Entropy explanation refer to file : "Decision Tree" in above Repo.

•Operates well in higher dimensions

•Avoids curse of dimensionality

•Fast to compute

•Max Margin: A slight error in measurement will not cause a misclassification

•fit line is the hyperplane that has a maximum number of points

•Y = mx +c

•-e < Ypred – Yact < e