diff --git a/README.md b/README.md
index 3384bf8..ec9989c 100644
--- a/README.md
+++ b/README.md
@@ -81,10 +81,10 @@ Thanks a lot for spending your time helping! Keep rocking π»
-
-
+ Siddhesh Bhupendra Kuakde Rahul Karda
@@ -110,10 +110,10 @@ Thanks a lot for spending your time helping! Keep rocking π»
-
-
+ Rahul Karda Noor Ahmed
@@ -138,13 +138,6 @@ Thanks a lot for spending your time helping! Keep rocking π»
Md Farhan Sajid
-
-
- Noor Ahmed
-
Raihan Khan
+
+
+ Siddhesh Bhupendra Kuakde
+
Null
+
+
+ Srishti
+
Avyay Jain
-
+
+
Drk1rd
-
-
+
Shradha
-
+
+
Nishant Pacharne
-
-
+
Olayinka Adeyemi
-
+
+
Parinthapat P.
-
-
+
Yash Indane
-
+
+
Ambush
-
-
+
Aiman Aisha
-
+
+
Akash Jain
-
-
+
Gokul_Zuzu
-
+
+
KUMAR SANTOSH
-
-
+
Ramon Saraiva
-
+
+
Riya Jaiswal
-
-
+
Ansh Rusia
-
+
+
Arpit Bhardwaj
-
-
+
Yash Nilesh Brid
-
+
+
Michele Mazza
-
-
+
Smit Shah
-
+
+
Sameer Sahu
-
-
+
-
+
\ No newline at end of file
diff --git a/scripts/Extract_TEXT_FROM_IMAGE/README.md b/scripts/Extract_TEXT_FROM_IMAGE/README.md
new file mode 100644
index 0000000..6c029cf
--- /dev/null
+++ b/scripts/Extract_TEXT_FROM_IMAGE/README.md
@@ -0,0 +1,16 @@
+# Extract text from image
+
+This script extracts text from an image using the EasyOCR library.
+It can support multiple languages.
+
+## Usage
+
+1. Create a virtual environment
+2. Install the requirements - `pip install -r requirements.txt`
+3. Run the script - `python main.py`
+
+## Output
+
+Output will be printed to console.
+
+Happy coding!!
\ No newline at end of file
diff --git a/scripts/Extract_TEXT_FROM_IMAGE/main.py b/scripts/Extract_TEXT_FROM_IMAGE/main.py
new file mode 100644
index 0000000..4821213
--- /dev/null
+++ b/scripts/Extract_TEXT_FROM_IMAGE/main.py
@@ -0,0 +1,15 @@
+import easyocr
+
+gpu = False # if you want to use GPU, set gpu=True
+languages = ['en'] # refer https://www.jaided.ai/easyocr/ for supporting languages
+
+reader = easyocr.Reader(languages, gpu=gpu)
+
+IMG_PATH = 'test1.png'
+result = reader.readtext(IMG_PATH)
+
+text = ''
+for tup in result:
+ text += tup[1]
+
+print(text)
\ No newline at end of file
diff --git a/scripts/Extract_TEXT_FROM_IMAGE/requirements.txt b/scripts/Extract_TEXT_FROM_IMAGE/requirements.txt
new file mode 100644
index 0000000..c6b2d0e
--- /dev/null
+++ b/scripts/Extract_TEXT_FROM_IMAGE/requirements.txt
@@ -0,0 +1 @@
+easyocr==1.6.2
\ No newline at end of file
diff --git a/scripts/Extract_TEXT_FROM_IMAGE/test1.png b/scripts/Extract_TEXT_FROM_IMAGE/test1.png
new file mode 100644
index 0000000..13a05bc
Binary files /dev/null and b/scripts/Extract_TEXT_FROM_IMAGE/test1.png differ
diff --git a/scripts/Loan Prediction Model/Loan Prediction Model .ipynb b/scripts/Loan Prediction Model/Loan Prediction Model .ipynb
new file mode 100644
index 0000000..80fcd0a
--- /dev/null
+++ b/scripts/Loan Prediction Model/Loan Prediction Model .ipynb
@@ -0,0 +1,1291 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8c97baae",
+ "metadata": {},
+ "source": [
+ "## Loan Prediction Model \n",
+ "\n",
+ "\n",
+ "The goal of this project is that from the data collected on the loanβs applicants, preprocess the data and predict based on the information who will be able to receive the loan or not.\n",
+ "\n",
+ "\n",
+ "In the Dataset we find the following features:\n",
+ "\n",
+ "1. Loan ID, the identifier code of each applicant.\n",
+ "2. Gender, Male or Female for each applicant.\n",
+ "3. Married, the maritage state.\n",
+ "4. Dependents, how many dependents does the applicant have?\n",
+ "5. Education, the level of education, graduate or non graduate\n",
+ "6. Self Employed, Yes or No in the case\n",
+ "7. Applicant Income\n",
+ "8. Coapplicant Income\n",
+ "9. Loan Amount\n",
+ "10. Loan Amount Term\n",
+ "11. Credit History, just Yes or No in the case\n",
+ "12. Property Area, urban, semiurban or rural area of the applicantβs property\n",
+ "\n",
+ "Loan Status, Yes or No ( The independent variable represents the class)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f28aeb7",
+ "metadata": {},
+ "source": [
+ "## Import Packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4cde977c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec208c3e",
+ "metadata": {},
+ "source": [
+ "## Read & visualize the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8895329b",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Loan_ID \n",
+ " Gender \n",
+ " Married \n",
+ " Dependents \n",
+ " Education \n",
+ " Self_Employed \n",
+ " ApplicantIncome \n",
+ " CoapplicantIncome \n",
+ " LoanAmount \n",
+ " Loan_Amount_Term \n",
+ " Credit_History \n",
+ " Property_Area \n",
+ " Loan_Status \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " LP001002 \n",
+ " Male \n",
+ " No \n",
+ " 0 \n",
+ " Graduate \n",
+ " No \n",
+ " 5849 \n",
+ " 0.0 \n",
+ " NaN \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " Urban \n",
+ " Y \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " LP001003 \n",
+ " Male \n",
+ " Yes \n",
+ " 1 \n",
+ " Graduate \n",
+ " No \n",
+ " 4583 \n",
+ " 1508.0 \n",
+ " 128.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " Rural \n",
+ " N \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " LP001005 \n",
+ " Male \n",
+ " Yes \n",
+ " 0 \n",
+ " Graduate \n",
+ " Yes \n",
+ " 3000 \n",
+ " 0.0 \n",
+ " 66.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " Urban \n",
+ " Y \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " LP001006 \n",
+ " Male \n",
+ " Yes \n",
+ " 0 \n",
+ " Not Graduate \n",
+ " No \n",
+ " 2583 \n",
+ " 2358.0 \n",
+ " 120.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " Urban \n",
+ " Y \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " LP001008 \n",
+ " Male \n",
+ " No \n",
+ " 0 \n",
+ " Graduate \n",
+ " No \n",
+ " 6000 \n",
+ " 0.0 \n",
+ " 141.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " Urban \n",
+ " Y \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ApplicantIncome \n",
+ " CoapplicantIncome \n",
+ " LoanAmount \n",
+ " Loan_Amount_Term \n",
+ " Credit_History \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 614.000000 \n",
+ " 614.000000 \n",
+ " 592.000000 \n",
+ " 600.00000 \n",
+ " 564.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 5403.459283 \n",
+ " 1621.245798 \n",
+ " 146.412162 \n",
+ " 342.00000 \n",
+ " 0.842199 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 6109.041673 \n",
+ " 2926.248369 \n",
+ " 85.587325 \n",
+ " 65.12041 \n",
+ " 0.364878 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 150.000000 \n",
+ " 0.000000 \n",
+ " 9.000000 \n",
+ " 12.00000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 2877.500000 \n",
+ " 0.000000 \n",
+ " 100.000000 \n",
+ " 360.00000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 3812.500000 \n",
+ " 1188.500000 \n",
+ " 128.000000 \n",
+ " 360.00000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 5795.000000 \n",
+ " 2297.250000 \n",
+ " 168.000000 \n",
+ " 360.00000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 81000.000000 \n",
+ " 41667.000000 \n",
+ " 700.000000 \n",
+ " 480.00000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "RangeIndex: 614 entries, 0 to 613\n",
+ "Data columns (total 13 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Loan_ID 614 non-null object \n",
+ " 1 Gender 601 non-null object \n",
+ " 2 Married 611 non-null object \n",
+ " 3 Dependents 599 non-null object \n",
+ " 4 Education 614 non-null object \n",
+ " 5 Self_Employed 582 non-null object \n",
+ " 6 ApplicantIncome 614 non-null int64 \n",
+ " 7 CoapplicantIncome 614 non-null float64\n",
+ " 8 LoanAmount 592 non-null float64\n",
+ " 9 Loan_Amount_Term 600 non-null float64\n",
+ " 10 Credit_History 564 non-null float64\n",
+ " 11 Property_Area 614 non-null object \n",
+ " 12 Loan_Status 614 non-null object \n",
+ "dtypes: float64(4), int64(1), object(8)\n",
+ "memory usage: 62.5+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "20168c69",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Loan_ID 0\n",
+ "Gender 13\n",
+ "Married 3\n",
+ "Dependents 15\n",
+ "Education 0\n",
+ "Self_Employed 32\n",
+ "ApplicantIncome 0\n",
+ "CoapplicantIncome 0\n",
+ "LoanAmount 22\n",
+ "Loan_Amount_Term 14\n",
+ "Credit_History 50\n",
+ "Property_Area 0\n",
+ "Loan_Status 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a52091bf",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Gender Married Dependents Education Self_Employed ApplicantIncome \\\n",
+ "0 1.0 0.0 0.0 1 0.0 5849 \n",
+ "1 1.0 1.0 1.0 1 0.0 4583 \n",
+ "2 1.0 1.0 0.0 1 1.0 3000 \n",
+ "3 1.0 1.0 0.0 0 0.0 2583 \n",
+ "4 1.0 0.0 0.0 1 0.0 6000 \n",
+ ".. ... ... ... ... ... ... \n",
+ "609 0.0 0.0 0.0 1 0.0 2900 \n",
+ "610 1.0 1.0 3.0 1 0.0 4106 \n",
+ "611 1.0 1.0 1.0 1 0.0 8072 \n",
+ "612 1.0 1.0 2.0 1 0.0 7583 \n",
+ "613 0.0 0.0 0.0 1 1.0 4583 \n",
+ "\n",
+ " CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History \\\n",
+ "0 0.0 NaN 360.0 1.0 \n",
+ "1 1508.0 128.0 360.0 1.0 \n",
+ "2 0.0 66.0 360.0 1.0 \n",
+ "3 2358.0 120.0 360.0 1.0 \n",
+ "4 0.0 141.0 360.0 1.0 \n",
+ ".. ... ... ... ... \n",
+ "609 0.0 71.0 360.0 1.0 \n",
+ "610 0.0 40.0 180.0 1.0 \n",
+ "611 240.0 253.0 360.0 1.0 \n",
+ "612 0.0 187.0 360.0 1.0 \n",
+ "613 0.0 133.0 360.0 0.0 \n",
+ "\n",
+ " Property_Area Loan_Status \n",
+ "0 1 1 \n",
+ "1 0 0 \n",
+ "2 1 1 \n",
+ "3 1 1 \n",
+ "4 1 1 \n",
+ ".. ... ... \n",
+ "609 0 1 \n",
+ "610 0 1 \n",
+ "611 1 1 \n",
+ "612 1 1 \n",
+ "613 2 0 \n",
+ "\n",
+ "[614 rows x 12 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Loan Status Encoding\n",
+ "df= df.replace({\"Loan_Status\":{'Y': 1, 'N': 0}})\n",
+ "\n",
+ "#Gender Encoding\n",
+ "df= df.replace({\"Gender\":{\"Male\":1, \"Female\":0 }})\n",
+ "\n",
+ "#Married Encoding\n",
+ "df =df.replace({\"Married\" :{\"Yes\":1, \"No\":0}})\n",
+ "\n",
+ "#Replace the 3+ in dependents ande make the column numeric\n",
+ "df['Dependents'] = df['Dependents'].replace('3+', '3')\n",
+ "df['Dependents']=pd.to_numeric(df['Dependents'], errors='coerce')\n",
+ "\n",
+ "#Count the quantity of values on the column\n",
+ "df['Self_Employed'].value_counts()\n",
+ "df= df.replace({\"Self_Employed\":{\"Yes\":1, \"No\":0 }})\n",
+ "\n",
+ "#Education Encoding\n",
+ "df['Education'].value_counts()\n",
+ "df= df.replace({\"Education\":{\"Graduate\":1, \"Not Graduate\":0 }})\n",
+ "\n",
+ "#Drop the Loan ID column\n",
+ "df = df.drop('Loan_ID',axis=1)\n",
+ "\n",
+ "#Property Area Encoding\n",
+ "df['Property_Area'].value_counts()\n",
+ "df['Property_Area'] = df['Property_Area'].map({'Rural': 0, 'Urban': 1, 'Semiurban': 2})\n",
+ "\n",
+ "print(df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "861ac719",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " \n",
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+ " 0.0 \n",
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+ " 360.0 \n",
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+ " \n",
+ " \n",
+ " 610 \n",
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+ " 1.0 \n",
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+ " 1 \n",
+ " 0.0 \n",
+ " 4106 \n",
+ " 0.0 \n",
+ " 40.0 \n",
+ " 180.0 \n",
+ " 1.0 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 611 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1 \n",
+ " 0.0 \n",
+ " 8072 \n",
+ " 240.0 \n",
+ " 253.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 612 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 2.0 \n",
+ " 1 \n",
+ " 0.0 \n",
+ " 7583 \n",
+ " 0.0 \n",
+ " 187.0 \n",
+ " 360.0 \n",
+ " 1.0 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 613 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 1 \n",
+ " 1.0 \n",
+ " 4583 \n",
+ " 0.0 \n",
+ " 133.0 \n",
+ " 360.0 \n",
+ " 0.0 \n",
+ " 2 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
614 rows Γ 12 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.set(rc={'figure.figsize':(15,8)})\n",
+ "sns.heatmap(df.corr(),annot=True,cmap=\"rocket\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "40bee983",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Married Education CoapplicantIncome Credit_History Property_Area \\\n",
+ "0 0.0 1 0.0 1.0 1 \n",
+ "1 1.0 1 1508.0 1.0 0 \n",
+ "2 1.0 1 0.0 1.0 1 \n",
+ "3 1.0 0 2358.0 1.0 1 \n",
+ "4 0.0 1 0.0 1.0 1 \n",
+ ".. ... ... ... ... ... \n",
+ "609 0.0 1 0.0 1.0 0 \n",
+ "610 1.0 1 0.0 1.0 0 \n",
+ "611 1.0 1 240.0 1.0 1 \n",
+ "612 1.0 1 0.0 1.0 1 \n",
+ "613 0.0 1 0.0 0.0 2 \n",
+ "\n",
+ " Loan_Status \n",
+ "0 1 \n",
+ "1 0 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 1 \n",
+ ".. ... \n",
+ "609 1 \n",
+ "610 1 \n",
+ "611 1 \n",
+ "612 1 \n",
+ "613 0 \n",
+ "\n",
+ "[614 rows x 6 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "def correlationdrop(df, sl):\n",
+ " columns = df.columns\n",
+ " for column in columns:\n",
+ " C=abs(df[column].corr(df['Loan_Status']))\n",
+ " if C < sl:\n",
+ " df=df.drop(columns=[column])\n",
+ " return df\n",
+ "\n",
+ "df= correlationdrop(df,0.05)\n",
+ "\n",
+ "print(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cd3d4a8",
+ "metadata": {},
+ "source": [
+ "## Separate the variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "c6e6d8cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = df.iloc[:,:-1].values\n",
+ "y = df.iloc[:,-1].values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e743143",
+ "metadata": {},
+ "source": [
+ "## Scale the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "b8992600",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "sc = MinMaxScaler()\n",
+ "X= sc.fit_transform(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3615ec24",
+ "metadata": {},
+ "source": [
+ "## Split the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "2a37ac15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size= 0.2, random_state= 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c98b35e0",
+ "metadata": {},
+ "source": [
+ "## Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "daba8de4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.linear_model import LogisticRegression\n",
+ "model=LogisticRegression()\n",
+ "model.fit(X_train,y_train)\n",
+ "z=model.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "16b8534b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8292682926829268"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "accuracy_score(y_test,z)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a31a652",
+ "metadata": {},
+ "source": [
+ "## SVM Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "e6c9e365",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.svm import SVC\n",
+ "classifier = SVC(kernel = 'rbf', gamma= 0.2)\n",
+ "classifier.fit(X_train, y_train)\n",
+ "\n",
+ "# Predicting the Test set results\n",
+ "y_pred = classifier.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5b880d7",
+ "metadata": {},
+ "source": [
+ "## Making the Confusion Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "a1503813",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[14 19]\n",
+ " [ 2 88]]\n",
+ "Accuracy: 80.44 %\n",
+ "Standard Deviation: 4.59 %\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "cm = confusion_matrix(y_test, y_pred)\n",
+ "print(cm)\n",
+ "\n",
+ "# Applying k-Fold Cross Validation\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
+ "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
+ "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/Loan Prediction Model/Readme.md b/scripts/Loan Prediction Model/Readme.md
new file mode 100644
index 0000000..eedbe79
--- /dev/null
+++ b/scripts/Loan Prediction Model/Readme.md
@@ -0,0 +1,22 @@
+## Loan Prediction Model
+
+
+The goal of this project is that from the data collected on the loanβs applicants, preprocess the data and predict based on the information who will be able to receive the loan or not.
+
+
+### In the Dataset we find the following features:
+
+1. Loan ID, the identifier code of each applicant.
+2. Gender, Male or Female for each applicant.
+3. Married, the maritage state.
+4. Dependents, how many dependents does the applicant have?
+5. Education, the level of education, graduate or non graduate
+6. Self Employed, Yes or No in the case
+7. Applicant Income
+8. Coapplicant Income
+9. Loan Amount
+10. Loan Amount Term
+11. Credit History, just Yes or No in the case
+12. Property Area, urban, semiurban or rural area of the applicantβs property
+
+Loan Status, Yes or No ( The independent variable represents the class)
diff --git a/scripts/Music Player/README.md b/scripts/Music Player/README.md
new file mode 100644
index 0000000..7aa576d
--- /dev/null
+++ b/scripts/Music Player/README.md
@@ -0,0 +1,13 @@
+# Music Player GUI
+
+
+
+This is a GUI framework where two GUI libraries are used:
+
+- Pygame
+- Tkinter
+
+It has functions such as play, stop, pause and resume in order to control the music player.
+
+To create a music player with Python, we will be using the **Pygame** sound component and _askdirectory()_ method of Tkinter
+
diff --git a/scripts/Music Player/music-player.py b/scripts/Music Player/music-player.py
new file mode 100644
index 0000000..3eb6fa2
--- /dev/null
+++ b/scripts/Music Player/music-player.py
@@ -0,0 +1,45 @@
+import pygame
+import tkinter as tkr
+from tkinter.filedialog import askdirectory
+import os
+
+music_player = tkr.Tk()
+music_player.title("My Music Player")
+music_player.geometry("450x350")
+directory = askdirectory()
+os.chdir(directory)
+song_list = os.listdir()
+
+play_list = tkr.Listbox(music_player, font="Helvetica 12 bold", bg='yellow', selectmode=tkr.SINGLE)
+for item in song_list:
+ pos = 0
+ play_list.insert(pos, item)
+ pos += 1
+pygame.init()
+pygame.mixer.init()
+
+def play():
+ pygame.mixer.music.load(play_list.get(tkr.ACTIVE))
+ var.set(play_list.get(tkr.ACTIVE))
+ pygame.mixer.music.play()
+def stop():
+ pygame.mixer.music.stop()
+def pause():
+ pygame.mixer.music.pause()
+def unpause():
+ pygame.mixer.music.unpause()
+Button1 = tkr.Button(music_player, width=5, height=3, font="Helvetica 12 bold", text="PLAY", command=play, bg="blue", fg="white")
+Button2 = tkr.Button(music_player, width=5, height=3, font="Helvetica 12 bold", text="STOP", command=stop, bg="red", fg="white")
+Button3 = tkr.Button(music_player, width=5, height=3, font="Helvetica 12 bold", text="PAUSE", command=pause, bg="purple", fg="white")
+Button4 = tkr.Button(music_player, width=5, height=3, font="Helvetica 12 bold", text="UNPAUSE", command=unpause, bg="orange", fg="white")
+
+var = tkr.StringVar()
+song_title = tkr.Label(music_player, font="Helvetica 12 bold", textvariable=var)
+
+song_title.pack()
+Button1.pack(fill="x")
+Button2.pack(fill="x")
+Button3.pack(fill="x")
+Button4.pack(fill="x")
+play_list.pack(fill="both", expand="yes")
+music_player.mainloop()
diff --git a/scripts/Music Player/requirements.txt b/scripts/Music Player/requirements.txt
new file mode 100644
index 0000000..d6ffd30
--- /dev/null
+++ b/scripts/Music Player/requirements.txt
@@ -0,0 +1,2 @@
+pygame
+tkinter
\ No newline at end of file
diff --git a/scripts/Quotes Fetch/readme.md b/scripts/Quotes Fetch/readme.md
new file mode 100644
index 0000000..c9fd724
--- /dev/null
+++ b/scripts/Quotes Fetch/readme.md
@@ -0,0 +1,5 @@
+## A program to fetch random quote or term related quote from internet
+
+use following command to search quote related to a specific term or phrase
+
+python scrape.py -q your_search_term -r yes
diff --git a/scripts/Quotes Fetch/scrape.py b/scripts/Quotes Fetch/scrape.py
new file mode 100644
index 0000000..f43518b
--- /dev/null
+++ b/scripts/Quotes Fetch/scrape.py
@@ -0,0 +1,35 @@
+#import section
+import requests
+from bs4 import BeautifulSoup as bs
+import argparse
+from random import choice
+
+# parser object to read cli content
+parser = argparse.ArgumentParser()
+parser.add_argument("-q", help="for search term")
+parser.add_argument("-r", help="if you want one random quote")
+
+args = parser.parse_args()
+
+
+search_term = args.q
+page = 1
+# URl definition
+url = f"https://www.brainyquote.com/search_results?q={search_term}&pg={page}"
+
+# main program
+r = requests.get(url)
+soup = bs(r.content, "html.parser")
+anchor1 = soup.find_all('a', {"class": "b-qt"})
+anchor2 = soup.find_all('a', {"class": "bq-aut"})
+
+
+if args.r != None:
+ quote = choice(
+ [f"{quote.text.strip()} - {author.text.strip()}" for quote, author in zip(anchor1, anchor2)])
+ print(quote)
+else:
+ for i, (quote, author) in enumerate(zip(anchor1, anchor2)):
+ quote = quote.find('div').text.strip()
+ author = author.text.strip()
+ print(f"[{i}] : {quote} - {author}")
diff --git a/scripts/Text_Game/Text_Game.py b/scripts/Text_Game/Text_Game.py
index 0a05b93..ae410e1 100644
--- a/scripts/Text_Game/Text_Game.py
+++ b/scripts/Text_Game/Text_Game.py
@@ -1,6 +1,11 @@
import random
import time
import sys
+# print(""" ββββββββββββββββββββ
+# βββββββββββ
+# β ββββββ
+# βββββββββ
+# βββββββββββ """)
def start():
# story generated
@@ -87,6 +92,7 @@ def random_select(rightpath):
if rightpath == str(select):
time.sleep(3)
print('good you went down the correct path and cheated death!!')
+ level2()
else:
time.sleep(3)
@@ -94,9 +100,147 @@ def random_select(rightpath):
time.sleep(3)
print('and you have been shot multiple time and are now dead.')
+def level2():
+ time.sleep(3)
+ print('You are running far away from the robots')
+ time.sleep(2)
+ print(""" ββββββββββββββββββββ
+ βββββββββββ
+ β ββββββ
+ βββββββββ
+ βββββββββββ """)
+ time.sleep(3)
+ print('and all of a sudden you see a cyborg in the distance')
+ time.sleep(2)
+ print("""ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+βββββββββββ¬βββ΄ββ¬ββ¬ββ΄βββ¬βββββββββββββββββ
+βββββββββββββββββββββββ€βββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββ€ββββββββββββββββββββββ
+βββββββββββββββ¬β΄ββ΄β¬ββ¬βββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ
+ββββββββββββββββββββββββββββββββββββββββ""")
+ time.sleep(3)
+ print('he has been sent to capture you and take you back to the robots!!!')
+ time.sleep(3)
+ print('three items are infront of you ')
+
+ time.sleep(3)
+ print("a mech suit")
+ time.sleep(2)
+ print("""βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+βββββββββββββββββββββββββββββββ
+""")
+
+ time.sleep(3)
+ print("tank")
+ time.sleep(2)
+ print("""βββββββββββββ ]ββββββββ
+ βββ
ββββββββββ
βββ
+ Iβββββββββββββββββββ].
+ β₯ββ²ββ²ββ²ββ²ββ²ββ²ββ€...
+ """)
+
+ time.sleep(3)
+ print('bomb')
+
+ time.sleep(2)
+ print("""´´´´´´´´´´´´´´´´´´´´´´´$¢´´´´´¢´´´´´¢’
+ ´´´´´´´´´´´¢¢¢¢¢¢¢´´´´´´´¢’´´´¢´´´ø¢
+ ´´´´´´´´´´¢¢´´´´ø¢¢¢´´´´´´oø´´ø´´øo
+ ´´´´´´´´´´¢7´´´´´´´¢¢¢´´´´´´1´´´1´´´´1o
+ ´´´´´´´¢¢¢¢¢¢¢´´´´´´´¢¢¢7´´´´´´´´1o¢¢¢ø
+ ´´´´´´´¢¢¢¢¢¢¢´´´´´´´´´¢¢¢¢¢¢¢¢´´1
+ ´´´´´o¢¢¢¢¢¢¢¢¢ø´´´´´´´´´´´´´´´´´´o$’
+ ´´´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢´´´´´´´´´´’´´1ø´´´1¢¢
+ ´´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢o´´´´´´´1$´´´¢
+ ´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢´´´´´´´¢´´´´o¢´
+ ´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢´´´´´¢¢
+ ´´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢´
+ ´´´¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢¢´
+ ´´´´´¢¢¢¢¢¢¢¢¢¢¢¢´
+ ´´´´´´´¢¢¢¢¢¢¢¢
+ """)
+
+ items = input("which item do you choose - the mech suit, tank or bomb : ")
+
+ if (items == 'tank'):
+ time.sleep(3)
+ print('oh no the cyborg has ripped through the tank')
+ time.sleep(3)
+ print('you are running away to find safety')
+ time.sleep(3)
+ print('but the cyborg has seen you and captured you')
+
+ elif (items == 'bomb'):
+ time.sleep(3)
+ print('you throw the bomb and it exlodes')
+ time.sleep(3)
+ print('but it doesnt even lay a scratch on him')
+ time.sleep(3)
+ print('because he is made from vibranium')
+ time.sleep(3)
+ print('you are instantly captured now')
+
+ elif (items == "mech suit"):
+ time.sleep(3)
+ print('you are now in the mech suit')
+ time.sleep(3)
+ print('the cyborg is running towards you')
+ time.sleep(3)
+ print('this suit is highly advanced and gives you a ton of diffrent options')
+
+ time.sleep(3)
+ select_option = input("what would you like to use: rocket fist, missile, body slam: ")
+
+ if (select_option == "rocket fist"):
+ time.sleep(3)
+ print('good the cyborg is extremely disorientated')
+ time.sleep(3)
+ combat_moves = input("do you want to throw a right hook or head butt: ")
+ if (combat_moves == 'right hook'):
+ time.sleep(3)
+ print('wow your right hook to the cyborg was so DEVASTATING that it has killed him')
+ elif (combat_moves == 'head butt'):
+ time.sleep(3)
+ print('incredible your head butt has KNOCKED OUT the cyborg')
+ elif (select_option == 'missile'):
+ time.sleep(3)
+ print('oh no the cyborg is made from vibranium')
+ time.sleep(3)
+ print('the cyborg punches you and takes you back to the robots')
+ elif (select_option == 'body slam'):
+ time.sleep(3)
+ print('the cyborg has been knocked unconsious good job')
+ time.sleep(3)
+ print('u have enough time to run')
# option to play again at completion of game
play_again = "yes"
diff --git a/scripts/fetching_system_information/README.md b/scripts/fetching_system_information/README.md
index 094b4f0..d4e6d86 100644
--- a/scripts/fetching_system_information/README.md
+++ b/scripts/fetching_system_information/README.md
@@ -1,6 +1,8 @@
# Fetching System Information
This is a simple script that fetches system information for your system.(Windows)
+## Improvements
+Script for fetching all systems information
## Usage
1. Clone the repo
2. Run python script.py
\ No newline at end of file
diff --git a/scripts/youtube_channel_info/README.md b/scripts/youtube_channel_info/README.md
new file mode 100644
index 0000000..1cb7017
--- /dev/null
+++ b/scripts/youtube_channel_info/README.md
@@ -0,0 +1,8 @@
+# Youtube Channel Info
+This is a simple script that fetches info about a youtube channel.
+
+## Usage
+- Clone the repo
+- download the requirements
+- add the api key and channel id
+- run python script.py
diff --git a/scripts/youtube_channel_info/script.py b/scripts/youtube_channel_info/script.py
new file mode 100644
index 0000000..4c3f14a
--- /dev/null
+++ b/scripts/youtube_channel_info/script.py
@@ -0,0 +1,18 @@
+from googleapiclient.discovery import build
+
+youtube = build('youtube', 'v3',
+ developerKey='Enter API key')
+
+ch_request = youtube.channels().list(
+ part='statistics',
+ id='Enter Channel ID')
+
+ch_response = ch_request.execute()
+
+sub = ch_response['items'][0]['statistics']['subscriberCount']
+vid = ch_response['items'][0]['statistics']['videoCount']
+views = ch_response['items'][0]['statistics']['viewCount']
+
+print("Total Subscriber:- ", sub)
+print("Total Number of Videos:- ", vid)
+print("Total Views:- ", views)
+
+ 
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Raihan Khan
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Null
+ 
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