Merge branch 'master' of https://github.com/AmirJlr/Data-Science-Algorithms-FUM

Author: AmirJlr

Classification/Linear Sepration/.ipynb_checkpoints/Simple Perceptron 1-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Classification/Linear Sepration/Simple Perceptron 1.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "e32c2611",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "6f3b12af",
    "metadata": {},
    "outputs": [
@@ -177,7 +177,7 @@
        "4                  0                1       1  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,16 +193,26 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "69df0a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.drop(['Unnamed: 0'] , axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "6f0bc787",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(10000, 14)"
+       "(10000, 13)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,14 +224,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "dab21ac2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Unnamed: 0           0\n",
        "CreditScore          0\n",
        "Age                  0\n",
        "Tenure               0\n",
@@ -238,7 +247,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "2811a2ea",
    "metadata": {},
    "outputs": [],
@@ -262,17 +271,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "8bbb9b19",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((10000, 13), (10000,))"
+       "((10000, 12), (10000,))"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "339032e6",
    "metadata": {},
    "outputs": [],
@@ -293,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "26095cde",
    "metadata": {},
    "outputs": [],
@@ -305,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "423dff79",
    "metadata": {},
    "outputs": [],
@@ -317,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "9cdcc500",
    "metadata": {},
    "outputs": [
@@ -326,22 +335,22 @@
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   20.3s remaining:    0.0s\n",
-      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:  1.4min finished\n",
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.5s remaining:    0.0s\n",
+      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:   49.6s finished\n",
       "\n",
-      "[2022-12-31 17:34:20] Features: 1/4 -- score: 0.8196999999999999[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   14.2s remaining:    0.0s\n",
-      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:  1.1min finished\n",
+      "[2023-01-25 00:12:20] Features: 1/4 -- score: 0.8196999999999999[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.8s remaining:    0.0s\n",
+      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:   45.2s finished\n",
       "\n",
-      "[2022-12-31 17:35:25] Features: 2/4 -- score: 0.8381000000000001[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.5s remaining:    0.0s\n",
-      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:   54.4s finished\n",
+      "[2023-01-25 00:13:06] Features: 2/4 -- score: 0.8391[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.9s remaining:    0.0s\n",
+      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   38.0s finished\n",
       "\n",
-      "[2022-12-31 17:36:19] Features: 3/4 -- score: 0.8526999999999999[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.1s remaining:    0.0s\n",
-      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  1.0min finished\n",
+      "[2023-01-25 00:13:44] Features: 3/4 -- score: 0.8513[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.5s remaining:    0.0s\n",
+      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   38.7s finished\n",
       "\n",
-      "[2022-12-31 17:37:22] Features: 4/4 -- score: 0.8548"
+      "[2023-01-25 00:14:23] Features: 4/4 -- score: 0.8539999999999999"
      ]
     }
    ],
@@ -351,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "1b137ab4",
    "metadata": {},
    "outputs": [
@@ -370,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "eb046ead",
    "metadata": {},
    "outputs": [
@@ -456,7 +465,7 @@
        "4   43              1               1                  0       0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -472,17 +481,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "3b8ecb17",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((10000, 5), (10000, 14))"
+       "((10000, 5), (10000, 13))"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }

Feature Selection/Forward Selection/forward1.ipynb

@@ -65,9 +65,9 @@ Eigenvalues are nothing but the amount of variance the factor explains.
 
 <h2>Factor Analysis Vs. Principle Component Analysis</h2>
 <ul>
+<li>PCA component is a linear combination of the observed variable while in FA, the observed variables are linear combinations of the unobserved variable or factor.</li>
   <li>PCA components explain the maximum amount of variance while factor analysis explains the covariance in data.</li>
   <li>PCA components are fully orthogonal to each other whereas factor analysis does not require factors to be orthogonal.</li>
-  <li>PCA component is a linear combination of the observed variable while in FA, the observed variables are linear combinations of the unobserved variable or factor.</li>
   <li>PCA components are uninterpretable. In FA, underlying factors are labelable and interpretable.</li>
   <li>PCA is a kind of dimensionality reduction method whereas factor analysis is the latent variable method.  </li>
   <li>PCA is a type of factor analysis. PCA is observational whereas FA is a modeling technique.</li>

Linear Model/FA/README.md

@@ -10,12 +10,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "057ee0dc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#import library\n",
+    "#import the libraries\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from sklearn.decomposition import PCA\n",

Linear Model/PCA/PCA 1.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Linear Model/SVD/.ipynb_checkpoints/SVD 1-checkpoint.ipynb

@@ -1 +1,2 @@
-<h1>svd </h1>
+<h1>svd </h1>
+The Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices. It has some interesting algebraic properties and conveys important geometrical and theoretical insights about linear transformations. It also has some important applications in data science. In this article, I will try to explain the mathematical intuition behind SVD and its geometrical meaning.