Model1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPcU+dDZsUdY8UFL/3nltk8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Step 1: Data Preparation"],"metadata":{"id":"q6priUPrQK9t"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":972},"id":"IKxozTfhO-Zt","executionInfo":{"status":"ok","timestamp":1716967468235,"user_tz":420,"elapsed":9029,"user":{"displayName":"Ajita Shrivastava","userId":"08943813694238378464"}},"outputId":"5ac6becc-5f1d-4fc3-8c72-aa923642317f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Financial Data\n","   Income  Expenses  Savings\n","0    4000      2500     1500\n","1    5000      2700     2300\n","2    6000      3000     3000\n","3    7000      3200     3800\n","4    8000      3500     4500\n","5    9000      3700     5300\n","6   10000      4000     6000\n","7   11000      4200     6800\n","8   12000      4500     7500\n","9   13000      4700     8300\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 750x750 with 12 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error\n","from sklearn.cluster import KMeans\n","import seaborn as sns\n","\n","# Sample financial data\n","data = {\n","    'Income': [4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000],\n","    'Expenses': [2500, 2700, 3000, 3200, 3500, 3700, 4000, 4200, 4500, 4700],\n","    'Savings': [1500, 2300, 3000, 3800, 4500, 5300, 6000, 6800, 7500, 8300]\n","}\n","\n","# Convert to DataFrame\n","df = pd.DataFrame(data)\n","\n","# Display the data\n","print(\"Financial Data\")\n","print(df)\n","\n","# Plot the data\n","sns.pairplot(df)\n","plt.show()\n"]},{"cell_type":"markdown","source":["Step 2: Preprocessing"],"metadata":{"id":"Px2vy53fQPhJ"}},{"cell_type":"code","source":["# Split the data into features and target variable\n","X = df[['Income', 'Expenses']]\n","y = df['Savings']\n","\n","# Split the dataset into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n","\n","# Standardize the features\n","scaler = StandardScaler()\n","X_train_scaled = scaler.fit_transform(X_train)\n","X_test_scaled = scaler.transform(X_test)\n"],"metadata":{"id":"Mvww30MtPF5_","executionInfo":{"status":"ok","timestamp":1716967484565,"user_tz":420,"elapsed":252,"user":{"displayName":"Ajita Shrivastava","userId":"08943813694238378464"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["Step 3: Linear Regression Model"],"metadata":{"id":"IbpZXrs2QUWP"}},{"cell_type":"code","source":["# Create and train the linear regression model\n","linear_regressor = LinearRegression()\n","linear_regressor.fit(X_train_scaled, y_train)\n","\n","# Predict on the test set\n","y_pred = linear_regressor.predict(X_test_scaled)\n","\n","# Evaluate the model\n","mse = mean_squared_error(y_test, y_pred)\n","print(f\"Mean Squared Error: {mse}\")\n","\n","# Compare actual vs predicted savings\n","comparison_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n","print(comparison_df)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ymfxCZkOPMt8","executionInfo":{"status":"ok","timestamp":1716967505078,"user_tz":420,"elapsed":179,"user":{"displayName":"Ajita Shrivastava","userId":"08943813694238378464"}},"outputId":"86a8f0e0-a246-49ac-fde6-ad8cc4be988b"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error: 1.0339757656912846e-25\n","   Actual  Predicted\n","2    3000     3000.0\n","8    7500     7500.0\n"]}]},{"cell_type":"markdown","source":["Step 4: K-means Clustering"],"metadata":{"id":"UjPgdT6hQWkK"}},{"cell_type":"code","source":["# Apply K-means clustering\n","kmeans = KMeans(n_clusters=3, random_state=0)\n","clusters = kmeans.fit_predict(df[['Income', 'Expenses', 'Savings']])\n","\n","# Add cluster information to the DataFrame\n","df['Cluster'] = clusters\n","\n","# Plot the clusters\n","sns.scatterplot(data=df, x='Income', y='Expenses', hue='Cluster', palette='viridis')\n","plt.title('K-means Clustering of Users')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":528},"id":"V5h7kmzzPQEL","executionInfo":{"status":"ok","timestamp":1716967518419,"user_tz":420,"elapsed":1148,"user":{"displayName":"Ajita Shrivastava","userId":"08943813694238378464"}},"outputId":"73058933-1c90-4a1f-deb0-e0c3cd1a2831"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Step 5: User Interface (Basic Console Input/Output)"],"metadata":{"id":"NGGHgDW5QZhd"}},{"cell_type":"code","source":["# Function to provide advice based on the user's data\n","def provide_financial_advice(income, expenses):\n","    # Standardize the user input\n","    user_data = scaler.transform([[income, expenses]])\n","\n","    # Predict savings\n","    predicted_savings = linear_regressor.predict(user_data)[0]\n","\n","    # Determine the cluster\n","    user_cluster = kmeans.predict([[income, expenses, predicted_savings]])[0]\n","\n","    advice = {\n","        0: \"You have a balanced financial status. Keep up the good work!\",\n","        1: \"You should consider reducing your expenses to increase your savings.\",\n","        2: \"Great job! Your savings are in good shape. Consider investing.\"\n","    }\n","\n","    return predicted_savings, advice[user_cluster]\n","\n","# Example user input\n","user_income = 9000\n","user_expenses = 3700\n","\n","predicted_savings, advice = provide_financial_advice(user_income, user_expenses)\n","print(f\"Predicted Savings: ${predicted_savings:.2f}\")\n","print(f\"Advice: {advice}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c_OUKdofP5hO","executionInfo":{"status":"ok","timestamp":1716967730188,"user_tz":420,"elapsed":170,"user":{"displayName":"Ajita Shrivastava","userId":"08943813694238378464"}},"outputId":"fd8a3b60-e283-452d-e1e7-e803d28a3b12"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Predicted Savings: $5300.00\n","Advice: You have a balanced financial status. Keep up the good work!\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"-1eTKaPpP7-R"},"execution_count":null,"outputs":[]}]}