Lead_Scoring_Algorithm_Notebook.ipynb

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   "source": [
    "![giskard_logo.png](https://raw.githubusercontent.com/Giskard-AI/giskard/main/readme/Logo_full_darkgreen.png)\n"
   ]
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   "source": [
    "# About Giskard\n",
    "\n",
    "Open-Source CI/CD platform for ML teams. Deliver ML products, better & faster. \n",
    "\n",
    "*   Collaborate faster with feedback from business stakeholders.\n",
    "*   Deploy automated tests to eliminate regressions, errors & biases.\n",
    "\n",
    "🏡 [Website](https://giskard.ai/)\n",
    "\n",
    "📗 [Documentation](https://docs.giskard.ai/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing `giskard`"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "!pip install giskard"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Connect the external worker in daemon mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!giskard worker start -d"
   ]
  },
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   "source": [
    "## Curious to know how it works ? \n",
    "#### Lets Start by creating our awesome Lead Scoring Algorithm Classification Model. 🎉🎉🎉"
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n"
   ]
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   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/Giskard-AI/examples/main/datasets/bank_lead_scoring_algorithm_updated.csv')"
   ]
  },
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   "source": [
    "data.head()"
   ]
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    "# Declare the type of each column in the dataset(example: category, numeric, text)\n",
    "column_types = {'age': 'numeric',\n",
    "                'job': 'category',\n",
    "                'marital_status': 'category',\n",
    "                'education': 'category',\n",
    "                'defaulted_bank_credit': 'category',\n",
    "                'avg_year_acc_balance': 'numeric',\n",
    "                'housing_loan': 'category',\n",
    "                'personal_loan':  'category',\n",
    "                'contacted_via': 'category',\n",
    "                'last_contact_dayofmonth': 'numeric',\n",
    "                'last_contact_month': 'category',\n",
    "                'last_call_duration_secs': 'numeric',\n",
    "                'num_contact_dur_campaign': 'numeric',\n",
    "                'dayscontact_since_lstcampaign': 'numeric',\n",
    "                'num_contact_bfr_campaign': 'numeric',\n",
    "                'outcome': 'category'\n",
    "                }"
   ]
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    "# feature_types is used to declare the features the model is trained on\n",
    "feature_types = {i:column_types[i] for i in column_types if i!='default'}\n",
    "\n",
    "# Pipeline to fill missing values, transform and scale the numeric columns\n",
    "numeric_features = [key for key in feature_types.keys() if feature_types[key]==\"numeric\"]\n",
    "numeric_transformer = Pipeline([('imputer', SimpleImputer(missing_values= np.nan, strategy='mean')),\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Pipeline to fill missing values and one hot encode the categorical values\n",
    "categorical_features = [key for key in feature_types.keys() if feature_types[key]==\"category\"]\n",
    "categorical_transformer = Pipeline([\n",
    "        ('imputer', SimpleImputer(missing_values= np.nan, strategy='most_frequent')),\n",
    "        ('onehot', OneHotEncoder(handle_unknown='ignore',sparse=False)) ])\n",
    "\n",
    "# Perform preprocessing of the columns with the above pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "      ('cat', categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")\n",
    "# Pipeline for the model Logistic Regression\n",
    "clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LogisticRegression(max_iter =1000))])"
   ]
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    "# Split the data into train and test\n",
    "y = data['will_subscribe']\n",
    "X = data.drop(columns=\"will_subscribe\")\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.20, random_state = 30, stratify = y)"
   ]
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   "source": [
    "# Fit and score your model\n",
    "clf.fit(X_train, y_train)\n",
    "print(\"model score: %.3f\" % clf.score(X_test, y_test))"
   ]
  },
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   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = clf.predict(X_test)\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)\n",
    "\n",
    "f1 = f1_score(y_true = y_test, y_pred = y_pred, average='macro')\n",
    "print('F1 Score: ', f1)\n",
    "\n",
    "accuracy = accuracy_score(y_true = y_test, y_pred = y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
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   "source": [
    "train_data = pd.concat([X_train, y_train], axis=1)\n",
    "test_data = pd.concat([X_test, y_test ], axis=1)"
   ]
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   "source": [
    "from giskard import GiskardClient\n",
    "\n",
    "url = \"http:https://localhost:19000\" #if Giskard is installed locally (for installation, see: https://docs.giskard.ai/start/guides/installation)\n",
    "#url = \"http:https://app.giskard.ai\" # If you want to upload on giskard URL\n",
    "token = \"YOUR GENERATED TOKEN\" #you can generate your API token in the Admin tab of the Giskard application (for installation, see: https://docs.giskard.ai/start/guides/installation)\n",
    "\n",
    "client = GiskardClient(url, token)\n",
    "\n",
    "# your_project = client.create_project(\"project_key\", \"PROJECT_NAME\", \"DESCRIPTION\")\n",
    "# Choose the arguments you want. But \"project_key\" should be unique and in lower case\n",
    "lead_scoring = client.create_project(\"lead_scoring\", \"Lead Scoring Scoring\", \"Project to predict if user will subscribe to the bank\")\n",
    "\n",
    "# If you've already created a project with the key \"lead_scoring\" use\n",
    "#credit_scoring = client.get_project(\"lead_scoring\")\n"
   ]
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   "source": [
    "lead_scoring.upload_model_and_df(\n",
    "    prediction_function=clf.predict_proba, # Python function which takes pandas dataframe as input and returns probabilities for classification model OR returns predictions for regression model\n",
    "    model_type='classification', # \"classification\" for classification model OR \"regression\" for regression model\n",
    "    df=test_data, # the dataset you want to use to inspect your model\n",
    "    column_types=column_types, # A dictionary with columns names of df as key and types(category, numeric, text) of columns as values\n",
    "    target='will_subscribe', # The column name in df corresponding to the actual target variable (ground truth).\n",
    "    feature_names=list(feature_types.keys()), # List of the feature names of prediction_function\n",
    "    classification_labels=clf.classes_,  # List of the classification labels of your prediction\n",
    "    model_name='logistic_regression_v1', # Name of the model\n",
    "    dataset_name='test_data' # Name of the dataset\n",
    ")"
   ]
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    "tags": []
   },
   "source": [
    "### Happy Exploration ! 🧑‍🚀"
   ]
  }
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